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3,000
|
SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics
|
https://openreview.net/forum?id=PQx66EJUu0
|
[
"Suyuan Zhao",
"YIZHEN LUO",
"Ganbo Yang",
"Yan Zhong",
"Hao Zhou",
"Zaiqing Nie"
] |
Poster
|
Spatial Transcriptomics (ST) technologies provide biologists with rich insights into single-cell biology by preserving spatial context of cells.
Building foundational models for ST can significantly enhance the analysis of vast and complex data sources, unlocking new perspectives on the intricacies of biological tissues.
However, modeling ST data is inherently challenging due to the need to extract multi-scale information from tissue slices containing vast numbers of cells. This process requires integrating macro-scale tissue morphology, micro-scale cellular microenvironment, and gene-scale gene expression profile.
To address this challenge, we propose **SToFM**, a multi-scale **S**patial **T**ranscript**o**mics **F**oundation **M**odel.
SToFM first performs multi-scale information extraction on each ST slice, to construct a set of ST sub-slices that aggregate macro-, micro- and gene-scale information. Then an SE(2) Transformer is used to obtain high-quality cell representations from the sub-slices.
Additionally, we construct **SToCorpus-88M**, the largest high-resolution spatial transcriptomics corpus for pretraining.
SToFM achieves outstanding performance on a variety of downstream tasks, such as tissue region semantic segmentation and cell type annotation, demonstrating its comprehensive understanding of ST data through capturing and integrating multi-scale information.
|
spatial transcriptomics, multi-scale, foundation model
|
We propose SToFM, a multi-scale foundation model for spatial transcriptomics, which can effectively capture and integrate multi-scale information in spatial transcriptomics data, establishing a comprehensive understanding of biological tissues.
| 272
| null |
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] |
https://github.com/PharMolix/SToFM
|
3,001
|
HyperIV: Real-time Implied Volatility Smoothing
|
https://openreview.net/forum?id=o9jtS7HupI
|
[
"Yongxin Yang",
"Wenqi Chen",
"Chao Shu",
"Timothy Hospedales"
] |
Poster
|
We propose HyperIV, a novel approach for real-time implied volatility smoothing that eliminates the need for traditional calibration procedures. Our method employs a hypernetwork to generate parameters for a compact neural network that constructs complete volatility surfaces within 2 milliseconds, using only 9 market observations. Moreover, the generated surfaces are guaranteed to be free of static arbitrage. Extensive experiments across 8 index options demonstrate that HyperIV achieves superior accuracy compared to existing methods while maintaining computational efficiency. The model also exhibits strong cross-asset generalization capabilities, indicating broader applicability across different market instruments. These key features -- rapid adaptation to market conditions, guaranteed absence of arbitrage, and minimal data requirements -- make HyperIV particularly valuable for real-time trading applications. We make code available at https://github.com/qmfin/hyperiv.
|
Implied volatility surface, quantitative finance, hypernetworks
|
Construct an arbitrage-free implied volatility surface from only 9 option contracts in 2 milliseconds.
| 271
| null |
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] |
https://github.com/qmfin/hyperiv
|
3,002
|
UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent
|
https://openreview.net/forum?id=V7JPraxi5j
|
[
"Jianke Zhang",
"Yanjiang Guo",
"Yucheng Hu",
"Xiaoyu Chen",
"Xiang Zhu",
"Jianyu Chen"
] |
Poster
|
Recent advancements in Vision-Language-Action (VLA) models have leveraged pre-trained Vision-Language Models (VLMs) to improve the generalization capabilities.
VLMs, typically pre-trained on vision-language understanding tasks, provide rich semantic knowledge and reasoning abilities.
However, prior research has shown that VLMs often focus on
high-level semantic content and neglect low-level features,
limiting their ability to capture detailed spatial information and understand physical dynamics.
These aspects, which are crucial for embodied control tasks, remain underexplored in existing pre-training paradigms.
In this paper, we investigate the training paradigm for VLAs, and
introduce \textbf{UP-VLA}, a \textbf{U}nified VLA model training with both multi-modal \textbf{U}nderstanding and future \textbf{P}rediction objectives, enhancing both high-level semantic comprehension and low-level spatial understanding. Experimental results show that UP-VLA achieves a 33\% improvement on the Calvin ABC-D benchmark compared to the previous state-of-the-art method. Additionally, UP-VLA demonstrates improved success rates in real-world manipulation tasks, particularly those requiring precise spatial information.
|
VLA, VLM, Embodied Agent
|
We propose UP-VLA，a unified understanding and prediction model for embodied agent.
| 267
| null |
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] |
https://github.com/CladernyJorn/UP-VLA
|
3,003
|
UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models
|
https://openreview.net/forum?id=EmLiyZGvrR
|
[
"Xin Xu",
"Qiyun Xu",
"Tong Xiao",
"Tianhao Chen",
"Yuchen Yan",
"Jiaxin ZHANG",
"Shizhe Diao",
"Can Yang",
"Yang Wang"
] |
Poster
|
Large language models (LLMs) have demonstrated remarkable capabilities in solving complex reasoning tasks, particularly in mathematics. However, the domain of physics reasoning presents unique challenges that have received significantly less attention. Existing benchmarks often fall short in evaluating LLMs’ abilities on the breadth and depth of undergraduate-level physics, underscoring the need for a comprehensive evaluation. To fill this gap, we introduce UGPhysics, a large-scale and diverse benchmark specifically designed to evaluate **U**nder**G**raduate-level **Physics** (**UGPhysics**) reasoning with LLMs. UGPhysics includes 5,520 undergraduate-level physics problems in both English and Chinese across 13 subjects with seven different answer types and four distinct physics reasoning skills, all rigorously screened for data leakage. Additionally, we develop a Model-Assistant Rule-based Judgment (**MARJ**) pipeline specifically tailored for assessing physics problems, ensuring accurate evaluation. Our evaluation of 31 leading LLMs shows that the highest overall accuracy, 49.8% (achieved by OpenAI-o1-mini), emphasizes the need for models with stronger physics reasoning skills, beyond math abilities. We hope UGPhysics, along with MARJ, will drive future advancements in AI for physics reasoning. Codes and data are available at \href{https://github.com/YangLabHKUST/UGPhysics}{https://github.com/YangLabHKUST/UGPhysics}.
|
Large Language Models, benchmark and dataset, physics reasoning
|
We introduce UGPhysics dataset along with MARJ answer assessment pipeline to advance AI for physics reasoning.
| 262
|
2502.00334
|
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] |
https://github.com/YangLabHKUST/UGPhysics
|
3,004
|
RBench: Graduate-level Multi-disciplinary Benchmarks for LLM & MLLM Complex Reasoning Evaluation
|
https://openreview.net/forum?id=BeoXADC9PW
|
[
"Meng-Hao Guo",
"Jiajun Xu",
"Yi Zhang",
"Jiaxi Song",
"Haoyang Peng",
"Yi-Xuan Deng",
"Xinzhi Dong",
"Kiyohiro Nakayama",
"Zhengyang Geng",
"Chen Wang",
"Bolin Ni",
"Guo-Wei Yang",
"Yongming Rao",
"Houwen Peng",
"Han Hu",
"Gordon Wetzstein",
"Shi-min Hu"
] |
Poster
|
Reasoning stands as a cornerstone of intelligence, enabling the synthesis of existing knowledge to solve complex problems. Despite remarkable progress, existing reasoning benchmarks often fail to rigorously evaluate the nuanced reasoning capabilities required for complex, real-world problemsolving, particularly in multi-disciplinary and multimodal contexts. In this paper, we introduce a graduate-level, multi-disciplinary, EnglishChinese benchmark, dubbed as Reasoning Bench (RBench), for assessing the reasoning capability of both language and multimodal models. RBench spans 1,094 questions across 108 subjects for language model evaluation and 665 questions across 83 subjects for multimodal model testing. These questions are meticulously curated to ensure rigorous difficulty calibration, subject balance, and cross-linguistic alignment, enabling the assessment to be an Olympiad-level multidisciplinary benchmark. We evaluate many models such as o1, GPT-4o, DeepSeek-R1, etc. Experimental results indicate that advanced models perform poorly on complex reasoning, especially multimodal reasoning. Even the top-performing model OpenAI o1 achieves only 53.2% accuracy on our multimodal evaluation. Data and code are made publicly available athttps://evalmodels.github.io/rbench/
|
Evaluation, Benchmark, Large Language Model, Multimodal Model, Reasoning.
|
An olympiad-level multi-disciplinary benchmark for complex reasoning evaluation
| 255
| null |
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] | null |
3,005
|
LLaVA-ReID: Selective Multi-image Questioner for Interactive Person Re-Identification
|
https://openreview.net/forum?id=c4EEnWu9FE
|
[
"Yiding Lu",
"Mouxing Yang",
"Dezhong Peng",
"Peng Hu",
"Yijie Lin",
"Xi Peng"
] |
Poster
|
Traditional text-based person ReID assumes that person descriptions from witnesses are complete and provided at once. However, in real-world scenarios, such descriptions are often partial or vague. To address this limitation, we introduce a new task called interactive person re-identification (Inter-ReID). Inter-ReID is a dialogue-based retrieval task that iteratively refines initial descriptions through ongoing interactions with the witnesses. To facilitate the study of this new task, we construct a dialogue dataset that incorporates multiple types of questions by decomposing fine-grained attributes of individuals. We further propose LLaVA-ReID, a question model that generates targeted questions based on visual and textual contexts to elicit additional details about the target person. Leveraging a looking-forward strategy, we prioritize the most informative questions as supervision during training. Experimental results on both Inter-ReID and text-based ReID benchmarks demonstrate that LLaVA-ReID significantly outperforms baselines.
|
Person Re-Identification, Interactive Retrieval
| null | 249
| null |
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] |
https://github.com/XLearning-SCU/LLaVA-ReID
|
3,006
|
ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models
|
https://openreview.net/forum?id=IYOPfJwduh
|
[
"Kangjie Zheng",
"Junwei Yang",
"Siyue Liang",
"Bin Feng",
"Zequn Liu",
"Wei Ju",
"Zhiping Xiao",
"Ming Zhang"
] |
Poster
|
Masked Language Models (MLMs) have achieved remarkable success in many self-supervised representation learning tasks. MLMs are trained by randomly masking portions of the input sequences with $\texttt{[MASK]}$ tokens and learning to reconstruct the original content based on the remaining context. This paper explores the impact of $\texttt{[MASK]}$ tokens on MLMs. Analytical studies show that masking tokens can introduce the ***corrupted semantics*** problem, wherein the corrupted context may convey multiple, ambiguous meanings. This problem is also a key factor affecting the performance of MLMs on downstream tasks. Based on these findings, we propose a novel enhanced-context MLM, ExLM. Our approach expands $\texttt{[MASK]}$ tokens in the input context and models the dependencies between these expanded states. This enhancement increases context capacity and enables the model to capture richer semantic information, effectively mitigating the corrupted semantics problem during pre-training. Experimental results demonstrate that ExLM achieves significant performance improvements in both text modeling and SMILES modeling tasks. Further analysis confirms that ExLM enriches semantic representations through context enhancement, and effectively reduces the semantic multimodality commonly observed in MLMs.
|
Masked Language Models, Pre-trained Models, Language Models, Text Modeling, SMILES Modeling
| null | 248
|
2501.13397
|
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] | null |
3,007
|
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
|
https://openreview.net/forum?id=b836TGkRSw
|
[
"Fabian Schaipp",
"Alexander Hägele",
"Adrien Taylor",
"Umut Simsekli",
"Francis Bach"
] |
Poster
|
We show that learning-rate schedules for large model training behave surprisingly similar to a performance bound from non-smooth convex optimization theory. We provide a bound for the constant schedule with linear cooldown; in particular, the practical benefit of cooldown is reflected in the bound due to the absence of logarithmic terms.
Further, we show that this surprisingly close match between optimization theory and practice can be exploited for learning-rate tuning: we achieve noticeable improvements for training 124M and 210M Llama-type models by (i) extending the schedule for continued training with optimal learning-rate, and (ii) transferring the optimal learning-rate across schedules.
|
Learning rate schedules, convex optimization theory, large model training, continual training
|
Learning rate schedules in deep learning behave strikingly similar to what convex optimization theory predicts
| 244
|
2501.18965
|
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] |
https://github.com/fabian-sp/lr-scheduling
|
3,008
|
A Theory for Conditional Generative Modeling on Multiple Data Sources
|
https://openreview.net/forum?id=OqOzcGBsqB
|
[
"Rongzhen Wang",
"Yan Zhang",
"Chenyu Zheng",
"Chongxuan Li",
"Guoqiang Wu"
] |
Poster
|
The success of large generative models has driven a paradigm shift, leveraging massive multi-source data to enhance model capabilities. However, the interaction among these sources remains theoretically underexplored. This paper provides a first attempt to fill this gap by rigorously analyzing multi-source training in conditional generative modeling, where each condition represents a distinct data source.
Specifically, we establish a general distribution estimation error bound in average total variation distance for conditional maximum likelihood estimation based on the bracketing number.
Our result shows that when source distributions share certain similarities and the model is expressive enough, multi-source training guarantees a sharper bound than single-source training.
We further instantiate the general theory on conditional Gaussian estimation and deep generative models including autoregressive and flexible energy-based models, by characterizing their bracketing numbers.
The results highlight that the number of sources and similarity among source distributions improve the advantage of multi-source training.
Simulations and real-world experiments validate our theory.
|
multiple data sources, generative model, distribution estimation, MLE
|
We analyze distribution estimation of conditional generative modeling on multiple data sources from the perspective of statistical learning theory.
| 243
|
2502.14583
|
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] |
https://github.com/ML-GSAI/Multi-Source-GM
|
3,009
|
Random Registers for Cross-Domain Few-Shot Learning
|
https://openreview.net/forum?id=4d2dwJN4v1
|
[
"Shuai Yi",
"Yixiong Zou",
"Yuhua Li",
"Ruixuan Li"
] |
Poster
|
Cross-domain few-shot learning (CDFSL) aims to transfer knowledge from a data-sufficient source domain to data-scarce target domains. Although Vision Transformer (ViT) has shown superior capability in many vision tasks, its transferability against huge domain gaps in CDFSL is still under-explored. In this paper, we find an intriguing phenomenon: during the source-domain training, prompt tuning, as a common way to train ViT, could be harmful for the generalization of ViT in target domains, but setting them to random noises (i.e., random registers) could consistently improve target-domain performance. We then delve into this phenomenon for an interpretation. We find that learnable prompts capture domain information during the training on the source dataset, which views irrelevant visual patterns as vital cues for recognition. This can be viewed as a kind of overfitting and increases the sharpness of the loss landscapes. In contrast, random registers are essentially a novel way of perturbing attention for the sharpness-aware minimization, which helps the model find a flattened minimum in loss landscapes, increasing the transferability. Based on this phenomenon and interpretation, we further propose a simple but effective approach for CDFSL to enhance the perturbation on attention maps by adding random registers on the semantic regions of image tokens, improving the effectiveness and efficiency of random registers. Extensive experiments on four benchmarks validate our rationale and state-of-the-art performance. Codes and models are available at https://github.com/shuaiyi308/REAP.
|
Cross-Domain Few-Shot Learning
|
We find prompt tuning, as a common way to train ViT, could consistently decrease the performance on target domains, while random noises can lead to an increase; we delve into this phenomenon for an interpretation, and propose a method based on it.
| 242
|
2506.02843
|
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] |
https://github.com/shuaiyi308/REAP
|
3,010
|
Clustering Properties of Self-Supervised Learning
|
https://openreview.net/forum?id=j7tKsPQotr
|
[
"Xi Weng",
"Jianing An",
"Xudong Ma",
"Binhang Qi",
"Jie Luo",
"Xi Yang",
"Jin Song Dong",
"Lei Huang"
] |
Poster
|
Self-supervised learning (SSL) methods via joint embedding architectures have proven remarkably effective at capturing semantically rich representations with strong clustering properties, magically in the absence of label supervision. Despite this, few of them have explored leveraging these untapped properties to improve themselves. In this paper, we provide an evidence through various metrics that the encoder's output *encoding* exhibits superior and more stable clustering properties compared to other components. Building on this insight, we propose a novel positive-feedback SSL method, termed **Re**presentation **S**elf-**A**ssignment (ReSA), which leverages the model's clustering properties to promote learning in a self-guided manner. Extensive experiments on standard SSL benchmarks reveal that models pretrained with ReSA outperform other state-of-the-art SSL methods by a significant margin. Finally, we analyze how ReSA facilitates better clustering properties, demonstrating that it effectively enhances clustering performance at both fine-grained and coarse-grained levels, shaping representations that are inherently more structured and semantically meaningful.
|
self-supervised learning, joint embedding architectures, clustering properties, positive-feedback learning
|
This paper proposes a novel positive-feedback SSL method, which leverages the model's clustering properties to promote learning in a self-guided manner.
| 240
|
2501.18452
|
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] |
https://github.com/winci-ai/resa
|
3,011
|
AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
|
https://openreview.net/forum?id=5PiZevq9fY
|
[
"Wenhao Sun",
"Rong-Cheng Tu",
"Jingyi Liao",
"Zhao Jin",
"Dacheng Tao"
] |
Poster
|
Diffusion Transformers (DiTs) have proven effective in generating high-quality videos but are hindered by high computational costs. Existing video diffusion sampling acceleration methods often rely on costly fine-tuning or exhibit limited generalization capabilities. We propose Asymmetric Reduction and Restoration (**AsymRnR**), **a training-free and model-agnostic method to accelerate video DiTs**. It builds on the observation that redundancies of feature tokens in DiTs vary significantly across different model blocks, denoising steps, and feature types. Our AsymRnR asymmetrically reduces redundant tokens in the attention operation, achieving acceleration with negligible degradation in output quality and, in some cases, even improving it. We also tailored a reduction schedule to distribute the reduction across components adaptively. To further accelerate this process, we introduce a matching cache for more efficient reduction. Backed by theoretical foundations and extensive experimental validation, AsymRnR integrates into state-of-the-art video DiTs and offers substantial speedup.
|
Video Generation, Diffusion Models, Video Transformers, Efficient Diffusion
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A training-free token-reduction approach to accelerate state-of-the-art video DiTs without compromising generation quality.
| 230
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2412.11706
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] |
https://github.com/wenhao728/AsymRnR
|
3,012
|
Epsilon-VAE: Denoising as Visual Decoding
|
https://openreview.net/forum?id=oK4ot2wAJU
|
[
"Long Zhao",
"Sanghyun Woo",
"Ziyu Wan",
"YANDONG LI",
"Han Zhang",
"Boqing Gong",
"Hartwig Adam",
"Xuhui Jia",
"Ting Liu"
] |
Poster
|
In generative modeling, tokenization simplifies complex data into compact, structured representations, creating a more efficient, learnable space. For high-dimensional visual data, it reduces redundancy and emphasizes key features for high-quality generation. Current visual tokenization methods rely on a traditional autoencoder framework, where the encoder compresses data into latent representations, and the decoder reconstructs the original input. In this work, we offer a new perspective by proposing denoising as decoding, shifting from single-step reconstruction to iterative refinement. Specifically, we replace the decoder with a diffusion process that iteratively refines noise to recover the original image, guided by the latents provided by the encoder. We evaluate our approach by assessing both reconstruction (rFID) and generation quality (FID), comparing it to state-of-the-art autoencoding approaches. By adopting iterative reconstruction through diffusion, our autoencoder, namely Epsilon-VAE, achieves high reconstruction quality, which in turn enhances downstream generation quality by 22% at the same compression rates or provides 2.3x inference speedup through increasing compression rates. We hope this work offers new insights into integrating iterative generation and autoencoding for improved compression and generation.
|
Diffusion Model, VAE, Image Tokenizer, Rectified Flow
| null | 217
| null |
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] | null |
3,013
|
ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think
|
https://openreview.net/forum?id=iPDw3O6u3T
|
[
"Tao Feng",
"Wei Li",
"Didi Zhu",
"Hangjie Yuan",
"Wendi Zheng",
"Dan Zhang",
"Jie Tang"
] |
Poster
|
Backpropagation provides a generalized configuration for overcoming catastrophic forgetting. Optimizers such as SGD and Adam are commonly used for weight updates in continual learning and continual pre-training. However, access to gradient information is not always feasible in practice due to black-box APIs, hardware constraints, or non-differentiable systems, a challenge we refer to as the gradient bans. To bridge this gap, we introduce ZeroFlow, the first benchmark designed to evaluate gradient-free optimization algorithms for overcoming forgetting. ZeroFlow examines a suite of forward pass-based methods across various algorithms, forgetting scenarios, and datasets. Our results show that forward passes alone can be sufficient to mitigate forgetting. We uncover novel optimization principles that highlight the potential of forward pass-based methods in mitigating forgetting, managing task conflicts, and reducing memory demands. Additionally, we propose new enhancements that further improve forgetting resistance using only forward passes. This work provides essential tools and insights to advance the development of forward-pass-based methods for continual learning.
|
Catastrophic Forgetting, Continual Learning, Incremental Learning
| null | 213
|
2501.01045
|
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] | null |
3,014
|
Dendritic Localized Learning: Toward Biologically Plausible Algorithm
|
https://openreview.net/forum?id=HBa4FcegJY
|
[
"Changze Lv",
"Jingwen Xu",
"Yiyang Lu",
"Xiaohua Wang",
"Zhenghua Wang",
"Zhibo Xu",
"Di Yu",
"Xin Du",
"Xiaoqing Zheng",
"Xuanjing Huang"
] |
Poster
|
Backpropagation is the foundational algorithm for training neural networks and a key driver of deep learning's success.
However, its biological plausibility has been challenged due to three primary limitations: weight symmetry, reliance on global error signals, and the dual-phase nature of training, as highlighted by the existing literature.
Although various alternative learning approaches have been proposed to address these issues, most either fail to satisfy all three criteria simultaneously or yield suboptimal results.
Inspired by the dynamics and plasticity of pyramidal neurons, we propose Dendritic Localized Learning (DLL), a novel learning algorithm designed to overcome these challenges.
Extensive empirical experiments demonstrate that DLL satisfies all three criteria of biological plausibility while achieving state-of-the-art performance among algorithms that meet these requirements.
Furthermore, DLL exhibits strong generalization across a range of architectures, including MLPs, CNNs, and RNNs.
These results, benchmarked against existing biologically plausible learning algorithms, offer valuable empirical insights for future research.
We hope this study can inspire the development of new biologically plausible algorithms for training multilayer networks and advancing progress in both neuroscience and machine learning.
Our code is available at https://github.com/Lvchangze/Dendritic-Localized-Learning.
|
Biological Plausibility, Learning Algorithm
|
We propose Dendritic Localized Learning (DLL), a biologically plausible learning algorithm.
| 209
|
2501.09976
|
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] |
https://github.com/Lvchangze/Dendritic-Localized-Learning
|
3,015
|
MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition
|
https://openreview.net/forum?id=AgAn6xtwWO
|
[
"Yuhuan Yang",
"Chaofan Ma",
"Zhenjie Mao",
"Jiangchao Yao",
"Ya Zhang",
"Yanfeng Wang"
] |
Poster
|
Video understanding is a complex challenge that requires effective modeling of spatial-temporal dynamics.
With the success of image foundation models (IFMs) in image understanding, recent approaches have explored parameter-efficient fine-tuning (PEFT) to adapt IFMs for video.
However, most of these methods tend to process
spatial and temporal information separately,
which may fail to capture the full intricacy of video dynamics.
In this paper, we propose MoMa, an efficient adapter framework that achieves full spatial-temporal modeling by integrating Mamba's selective state space modeling into IFMs.
We propose a novel SeqMod operation to inject spatial-temporal information into pre-trained IFMs, without disrupting their original features.
By incorporating SeqMod into a Divide-and-Modulate architecture, MoMa enhances video understanding while maintaining computational efficiency.
Extensive experiments on multiple video benchmarks demonstrate the effectiveness of MoMa, achieving superior performance with reduced computational cost.
Codes will be released upon publication.
|
Video Recognition, PEFT, Mamba, Adapter
|
We propose MoMa, an efficient adapter that integrates spatial-temporal modeling into pre-trained image foundation models for improved video understanding, achieving better performance with lower computational cost.
| 202
|
2506.23283
|
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] | null |
3,016
|
Ultra-Resolution Adaptation with Ease
|
https://openreview.net/forum?id=4skvLszxZ1
|
[
"Ruonan Yu",
"Songhua Liu",
"Zhenxiong Tan",
"Xinchao Wang"
] |
Poster
|
Text-to-image diffusion models have achieved remarkable progress in recent years. However, training models for high-resolution image generation remains challenging, particularly when training data and computational resources are limited. In this paper, we explore this practical problem from two key perspectives: data and parameter efficiency, and propose a set of key guidelines for ultra-resolution adaptation termed URAE. For data efficiency, we theoretically and empirically demonstrate that synthetic data generated by some teacher models can significantly promote training convergence. For parameter efficiency, we find that tuning minor components of the weight matrices outperforms widely-used low-rank adapters when synthetic data are unavailable, offering substantial performance gains while maintaining efficiency. Additionally, for models leveraging guidance distillation, such as FLUX, we show that disabling classifier-free guidance, i.e., setting the guidance scale to 1 during adaptation, is crucial for satisfactory performance. Extensive experiments validate that URAE achieves comparable 2K-generation performance to state-of-the-art closed-source models like FLUX1.1 [Pro] Ultra with only 3K samples and 2K iterations, while setting new benchmarks for 4K-resolution generation. Codes are available [here](https://github.com/Huage001/URAE).
|
Ultra-Resolution Generation, Efficient Tuning, Synthetic Data, Diffusion Models
|
We present key factors for adapting a pre-trained text-to-image diffusion model to ultra resolution with data and parameter efficiency.
| 200
|
2503.16322
|
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] |
https://github.com/Huage001/URAE
|
3,017
|
From Spectrum-free towards Baseline-view-free: Double-track Proximity Driven Multi-view Clustering
|
https://openreview.net/forum?id=8tE4DkYUex
|
[
"Shengju Yu",
"Zhibin Dong",
"Siwei Wang",
"Suyuan Liu",
"KE LIANG",
"Xinwang Liu",
"Yue Liu",
"Yi Zhang"
] |
Poster
|
Current multi-view clustering (MVC) techniques generally focus only on the relationship between anchors and samples, while overlooking that between anchors. Moreover, due to the lack of data labels, the cluster order is inconsistent across views and accordingly anchors encounter misalignment, which will confuse the graph structure and disorganize cluster representation. Even worse, it typically brings variance during forming spectral embedding, degenerating the stability of clustering results. In response to these concerns, in the paper we propose a MVC approach named DTP-SF-BVF. Concretely, we explicitly exploit the geometric properties between anchors via self-expression learning skill, and utilize topology learning strategy to feed captured anchor-anchor features into anchor-sample graph so as to explore the manifold structure hidden within samples more adequately. To reduce the misalignment risk, we introduce a permutation mechanism for each view to jointly rearrange anchors according to respective view characteristics. Besides not involving selecting the baseline view, it also can coordinate with anchors in the unified framework and thereby facilitate the learning of anchors. Further, rather than forming spectrum and then performing embedding partitioning, based on the criterion that samples and clusters should be hard assignment, we manage to construct the cluster labels directly from original samples using the binary strategy, not only preserving the data diversity but avoiding variance. Experiments on multiple publicly available datasets confirm the effectiveness of proposed DTP-SF-BVF method.
|
Multi-view Clustering, Anchor, Subspace Learning
| null | 186
| null |
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] | null |
3,018
|
Exactly Tight Information-theoretic Generalization Bounds via Binary Jensen-Shannon Divergence
|
https://openreview.net/forum?id=qfgtBeBLsD
|
[
"Yuxin Dong",
"Haoran Guo",
"Tieliang Gong",
"Wen Wen",
"Chen Li"
] |
Poster
|
Information-theoretic bounds, while achieving significant success in analyzing the generalization of randomized learning algorithms, have been criticized for their slow convergence rates and overestimation. This paper presents novel bounds that bridge the expected empirical and population risks through a binarized variant of the Jensen-Shannon divergence. Leveraging our foundational lemma that characterizes the interaction between an arbitrary and a binary variable, we derive hypothesis-based bounds that enhance existing conditional mutual information bounds by reducing the number of conditioned samples from $2$ to $1$. We additionally establish prediction-based bounds that surpass prior bounds based on evaluated loss mutual information measures. Thereafter, through a new binarization technique for the evaluated loss variables, we obtain exactly tight generalization bounds broadly applicable to general randomized learning algorithms for any bounded loss functions. Our results effectively address key limitations of previous results in analyzing certain stochastic convex optimization problems, without requiring additional stability or compressibility assumptions about the learning algorithm.
|
Information Theory, Generalization Analysis, Mutual Information, Jensen-Shannon Divergence
|
We establish exactly tight information-theoretic generalization bounds for general randomized learning algorithms.
| 185
| null |
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] |
https://github.com/Yuxin-Dong/BinaryJS
|
3,019
|
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
|
https://openreview.net/forum?id=yV9MbLCt30
|
[
"Kai Liu",
"Kaicheng Yang",
"Zheng Chen",
"Zhiteng Li",
"Yong Guo",
"Wenbo Li",
"Linghe Kong",
"Yulun Zhang"
] |
Poster
|
While super-resolution (SR) methods based on diffusion models (DM) have demonstrated inspiring performance, their deployment is impeded due to the heavy request of memory and computation. Recent researchers apply two kinds of methods to compress or fasten the DM. One is to compress the DM into 1-bit, aka binarization, alleviating the storage and computation pressure. The other distills the multi-step DM into only one step, significantly speeding up inference process. Nonetheless, it remains impossible to deploy DM to resource-limited edge devices. To address this problem, we propose BiMaCoSR, which combines binarization and one-step distillation to obtain extreme compression and acceleration. To prevent the catastrophic collapse of the model caused by binarization, we proposed sparse matrix branch (SMB) and low rank matrix branch (LRM). Both auxiliary branches pass the full-precision (FP) information but in different ways. SMB absorbs the extreme values and its output is high rank, carrying abundant FP information. Whereas, the design of LRMB is inspired by LoRA and is initialized with the top r SVD components, outputting low rank representation. The computation and storage overhead of our proposed branches can be safely ignored. Comprehensive comparison experiments are conducted to exhibit BiMaCoSR outperforms current state-of-the-art binarization methods and gains competitive performance compared with FP one-step model. Moreover, we achieve excellent compression and acceleration. BiMaCoSR achieves a 23.8x compression ratio and a 27.4x speedup ratio compared to FP counterpart. Our code and model are available at https://github.com/Kai-Liu001/BiMaCoSR
|
super-resolution, one-step diffusion, binarization, quantization
|
A binarized one-step diffusion model for super-resolution.
| 183
|
2502.00333
|
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] |
https://github.com/Kai-Liu001/BiMaCoSR
|
3,020
|
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
|
https://openreview.net/forum?id=EhStXG4dCS
|
[
"Thibault de Surrel",
"Fabien Lotte",
"Sylvain Chevallier",
"Florian Yger"
] |
Poster
|
Circular and non-flat data distribution are prevalent across diverse domains of data science, yet their specific geometric structures often remain underutilized in machine learning frameworks.
A principled approach to accounting for the underlying geometry of such data is pivotal, particularly when extending statistical models, like the pervasive Gaussian distribution.
In this work, we tackle those issue by focusing on the manifold of symmetric positive definite matrices, a key focus in information geometry.
We introduced a non-isotropic wrapped Gaussian by leveraging the exponential map, we derive theoretical properties of this distribution and propose a maximum likelihood framework for parameter estimation. Furthermore, we reinterpret established classifiers on SPD through a probabilistic lens and introduce new classifiers based on the wrapped Gaussian model.
Experiments on synthetic and real-world datasets demonstrate the robustness and flexibility of this geometry-aware distribution, underscoring its potential to advance manifold-based data analysis.
This work lays the groundwork for extending classical machine learning and statistical methods to more complex and structured data.
|
Gaussian distribution, Wrapped distributions, Symmetric positive definite matrices, estimation, classification, Riemannian geometry, density estimation
|
We propose and study the properties and the estimation of wrapped Gaussian distributions on the manifold of SPD matrices
| 181
|
2502.01512
|
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] |
https://github.com/thibaultdesurrel/wrapped_gaussians_SPD
|
3,021
|
BaWA: Automatic Optimizing Pruning Metric for Large Language Models with Balanced Weight and Activation
|
https://openreview.net/forum?id=YrCvW1Hx7g
|
[
"Lian Liu",
"Xiandong Zhao",
"Guanchen Li",
"Dong Li",
"Mengdi Wang",
"Yinhe Han",
"Xiaowei Li",
"ying wang"
] |
Poster
|
One-shot post-training pruning enhances the deployment of billion-scale large language models (LLMs), with the pruning metric playing a pivotal role in determining which weights to remove. However, existing metrics underperform due to their reliance on a simple symbolic combination of weights and activations, overlooking imbalanced weight magnitudes and the disproportionate influence of activation outliers.
To overcome these limitations, we introduce \textbf{BaWA}, a novel pruning metric that systematically \underline{Ba}lances \underline{W}eight and \underline{A}ctivation distributions for more effective pruning.
BaWA introduces two key innovations: \textbf{magnitude normalization}, which mitigates weight imbalance across channels for fairer pruning decisions, and \textbf{outlier regularization}, which reduces the impact of activation outliers, ensuring more appropriate channel prioritization.
To further enhance its effectiveness, BaWA incorporates an efficient and automatic framework for optimizing normalization and regularization hyperparameters. Extensive experiments validate BaWA as a state-of-the-art (SOTA) pruning metric. For instance, applying BaWA to induce 2:4 sparsity in Mistral-7B reduces perplexity in language comprehension by 2.49 and improves average downstream task accuracy by 3.08\%, outperforming the previous SOTA method Wanda.
|
LLM Pruning; Automaic Framework
|
We identify the existing bias in existing pruning metric and propose a novel automatic optimization framework to get the appropriate pruning metric for better pruning results.
| 178
| null |
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] | null |
3,022
|
Action Dubber: Timing Audible Actions via Inflectional Flow
|
https://openreview.net/forum?id=9OjTHu4sea
|
[
"Wenlong Wan",
"Weiying Zheng",
"Tianyi Xiang",
"Guiqing Li",
"Shengfeng He"
] |
Poster
|
We introduce the task of Audible Action Temporal Localization, which aims to identify the spatio-temporal coordinates of audible movements. Unlike conventional tasks such as action recognition and temporal action localization, which broadly analyze video content, our task focuses on the distinct kinematic dynamics of audible actions. It is based on the premise that key actions are driven by inflectional movements; for example, collisions that produce sound often involve abrupt changes in motion. To capture this, we propose $TA^{2}Net$, a novel architecture that estimates inflectional flow using the second derivative of motion to determine collision timings without relying on audio input. $TA^{2}Net$ also integrates a self-supervised spatial localization strategy during training, combining contrastive learning with spatial analysis. This dual design improves temporal localization accuracy and simultaneously identifies sound sources within video frames. To support this task, we introduce a new benchmark dataset, $Audible623$, derived from Kinetics and UCF101 by removing non-essential vocalization subsets. Extensive experiments confirm the effectiveness of our approach on $Audible623$ and show strong generalizability to other domains, such as repetitive counting and sound source localization. Code and dataset are available at https://github.com/WenlongWan01/Audible623.
|
Video Understanding, Temporal Localization
|
A new method and dataset utilizing inflectional flow to address Audible Action Temporal Localization problem.
| 154
|
2506.13320
|
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] |
https://github.com/WenlongWan01/Audible623
|
3,023
|
Visual Autoregressive Modeling for Image Super-Resolution
|
https://openreview.net/forum?id=JPTpUGJDGY
|
[
"Yunpeng Qu",
"Kun Yuan",
"Jinhua Hao",
"Kai Zhao",
"Qizhi Xie",
"Ming Sun",
"Chao Zhou"
] |
Poster
|
Image Super-Resolution (ISR) has seen significant progress with the introduction of remarkable generative models.
However, challenges such as the trade-off issues between fidelity and realism, as well as computational complexity, have also posed limitations on their application.
Building upon the tremendous success of autoregressive models in the language domain, we propose \textbf{VARSR}, a novel visual autoregressive modeling for ISR framework with the form of next-scale prediction.
To effectively integrate and preserve semantic information in low-resolution images, we propose using prefix tokens to incorporate the condition.
Scale-aligned Rotary Positional Encodings are introduced to capture spatial structures and the diffusion refiner is utilized for modeling quantization residual loss to achieve pixel-level fidelity.
Image-based Classifier-free Guidance is proposed to guide the generation of more realistic images.
Furthermore, we collect large-scale data and design a training process to obtain robust generative priors.
Quantitative and qualitative results show that VARSR is capable of generating high-fidelity and high-realism images with more efficiency than diffusion-based methods.
Our codes are released at \url{https://github.com/quyp2000/VARSR}.
|
visual autoregressive modeling; image super-resolution
|
This paper introduces VARSR, a novel visual autoregressive framework for image super-resolution.
| 139
|
2501.18993
|
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] |
https://github.com/quyp2000/VARSR
|
3,024
|
Gamma Distribution PCA-Enhanced Feature Learning for Angle-Robust SAR Target Recognition
|
https://openreview.net/forum?id=si1iynl5y7
|
[
"Chong Zhang",
"Peng Zhang",
"Mengke Li"
] |
Poster
|
Scattering characteristics of synthetic aperture radar (SAR) targets are typically related to observed azimuth and depression angles.
However, in practice, it is difficult to obtain adequate training samples at all observation angles, which probably leads to poor robustness of deep networks. In this paper, we first propose a Gamma-Distribution Principal Component Analysis ($\Gamma$PCA) model that fully accounts for the statistical characteristics of SAR data. The $\Gamma$PCA derives consistent convolution kernels to effectively capture the angle-invariant features of the same target at various attitude angles, thus alleviating deep models' sensitivity to angle changes in SAR target recognition task. We validate $\Gamma$PCA model based on two commonly used backbones, ResNet and ViT, and conduct multiple robustness experiments on the MSTAR benchmark dataset. The experimental results demonstrate that $\Gamma$PCA effectively enables the model to withstand substantial distributional discrepancy caused by angle changes. Additionally, $\Gamma$PCA convolution kernel is designed to require no parameter updates, introducing no extra computational burden to the network. The source code is available at \href{https://github.com/ChGrey/GammaPCA}{https://github.com/ChGrey/GammaPCA}.
|
Synthetic aperture radar; Automatic target recognition
| null | 132
| null |
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] |
https://github.com/ChGrey/GammaPCA
|
3,025
|
Scaling Laws for Floating–Point Quantization Training
|
https://openreview.net/forum?id=83VDYpSd8R
|
[
"Xingwu Sun",
"Shuaipeng Li",
"Ruobing Xie",
"Weidong Han",
"Kan Wu",
"Zhen Yang",
"Yixing Li",
"An Wang",
"SHUAI LI",
"Jinbao Xue",
"Yu Cheng",
"Yangyu Tao",
"Zhanhui Kang",
"Cheng-zhong Xu",
"Di Wang",
"Jie Jiang"
] |
Poster
|
Low-precision training is considered an effective strategy for reducing both training and downstream inference costs. Previous scaling laws for precision mainly focus on integer quantization, which pay less attention to the constituents in floating-point (FP) quantization, and thus cannot well fit the LLM losses in this scenario. In contrast, while FP quantization training is more commonly implemented in production, it's research has been relatively superficial. In this paper, we thoroughly explore the effects of FP quantization targets, exponent bits, mantissa bits, and the calculation granularity of the scaling factor in FP quantization training performance of LLM models.
In addition to an accurate FP quantization unified scaling law, we also provide valuable suggestions for the community: (1) Exponent bits contribute slightly more to the model performance than mantissa bits. We provide the optimal exponent-mantissa bit ratio for different bit numbers, which is available for future reference by hardware manufacturers; (2) We discover the formation of the critical data size in low-precision LLM training. Too much training data exceeding the critical data size will inversely bring in degradation of LLM performance; (3) The optimal FP quantization precision is directly proportional to the computational power, but within a wide computational power range. We estimate that the best cost-performance precision should lie between 4-8 bits.
|
Floating-point quantization, Scaling law, Training performance
|
This paper examines floating-point quantization in LLM training, proposes a unified scaling law, and offers key recommendations for improving model performance and cost-efficiency.
| 128
|
2501.02423
|
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0.03836704418063164,
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] | null |
3,026
|
Enhancing Target-unspecific Tasks through a Features Matrix
|
https://openreview.net/forum?id=JD45c59Q09
|
[
"Fangming Cui",
"Yonggang Zhang",
"Xuan Wang",
"Xinmei Tian",
"Jun Yu"
] |
Poster
|
Recent developments in prompt learning of large Vision-Language Models (VLMs) have significantly improved performance in target-specific tasks. However, these prompting methods often struggle to tackle the target-unspecific or generalizable tasks effectively.
It may be attributed to the fact that overfitting training causes the model to forget its general knowledge. The general knowledge has a strong promotion on target-unspecific tasks. To alleviate this issue, we propose a novel Features Matrix (FM) approach designed to enhance these models on target-unspecific tasks. Our method extracts and leverages general knowledge, shaping a Features Matrix (FM). Specifically, the FM captures the semantics of diverse inputs from a deep and fine perspective, preserving essential general knowledge, which mitigates the risk of overfitting. Representative evaluations demonstrate that: 1) the FM is compatible with existing frameworks as a generic and flexible module, and 2) the FM significantly showcases its effectiveness in enhancing target-unspecific tasks (base-to-novel generalization, domain generalization, and cross-dataset generalization), achieving state-of-the-art performance.
|
Prompt learning
|
Enhancing Target-unspecific Tasks through a Features Matrix
| 123
|
2505.03414
|
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] | null |
3,027
|
Tackling View-Dependent Semantics in 3D Language Gaussian Splatting
|
https://openreview.net/forum?id=oaMDLJezM4
|
[
"Jiazhong Cen",
"Xudong Zhou",
"Jiemin Fang",
"Changsong Wen",
"Lingxi Xie",
"XIAOPENG ZHANG",
"Wei Shen",
"Qi Tian"
] |
Poster
|
Recent advancements in 3D Gaussian Splatting (3D-GS) enable high-quality 3D scene reconstruction from RGB images. Many studies extend this paradigm for language-driven open-vocabulary scene understanding. However, most of them simply project 2D semantic features onto 3D Gaussians and overlook a fundamental gap between 2D and 3D understanding: a 3D object may exhibit various semantics from different viewpoints—a phenomenon we term **view-dependent semantics**. To address this challenge, we propose **LaGa** (**La**nguage **Ga**ussians), which establishes cross-view semantic connections by decomposing the 3D scene into objects. Then, it constructs view-aggregated semantic representations by clustering semantic descriptors and reweighting them based on multi-view semantics. Extensive experiments demonstrate that LaGa effectively captures key information from view-dependent semantics, enabling a more comprehensive understanding of 3D scenes. Notably, under the same settings, LaGa achieves a significant improvement of **+18.7\% mIoU** over the previous SOTA on the LERF-OVS dataset. Our code is available at: https://github.com/https://github.com/SJTU-DeepVisionLab/LaGa.
|
3D Gaussian splatting; open-vocabulary scene understanding; 3D scene understanding
|
This paper presents LaGa, a language-driven open-vocabulary 3D scene understanding method built upon 3D Gaussian splatting, designed to effectively handle the view dependency of 3D semantics.
| 112
|
2505.24746
|
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] |
https://github.com/SJTU-DeepVisionLab/LaGa
|
3,028
|
Rethinking Time Encoding via Learnable Transformation Functions
|
https://openreview.net/forum?id=pnZq5FojHH
|
[
"Xi Chen",
"Yateng Tang",
"Jiarong Xu",
"Jiawei Zhang",
"Siwei Zhang",
"Sijia Peng",
"Xuehao Zheng",
"Yun Xiong"
] |
Poster
|
Effectively modeling time information and incorporating it into applications or models involving chronologically occurring events is crucial. Real-world scenarios often involve diverse and complex time patterns, which pose significant challenges for time encoding methods. While previous methods focus on capturing time patterns, many rely on specific inductive biases, such as using trigonometric functions to model periodicity. This narrow focus on single-pattern modeling makes them less effective in handling the diversity and complexities of real-world time patterns. In this paper, we investigate to improve the existing commonly used time encoding methods and introduce **Learnable Transformation-based Generalized Time Encoding (LeTE)**. We propose using deep function learning techniques to parameterize nonlinear transformations in time encoding, making them learnable and capable of modeling generalized time patterns, including diverse and complex temporal dynamics. By enabling learnable transformations, LeTE encompasses previous methods as specific cases and allows seamless integration into a wide range of tasks. Through extensive experiments across diverse domains, we demonstrate the versatility and effectiveness of LeTE.
|
Time Encoding, Dynamic Graph, Time Series, Deep Function Learning
|
Generalizing time encoding in diverse domains through learnable transformation functions
| 103
|
2505.00887
|
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] |
https://github.com/chenxi1228/LeTE
|
3,029
|
Delta Decompression for MoE-based LLMs Compression
|
https://openreview.net/forum?id=ziezViPoN1
|
[
"Hao Gu",
"Wei Li",
"Lujun Li",
"Zhu Qiyuan",
"Mark G. Lee",
"Shengjie Sun",
"Wei Xue",
"Yike Guo"
] |
Poster
|
Mixture-of-Experts (MoE) architectures in large language models (LLMs) achieve exceptional performance, but face prohibitive storage and memory requirements. To address these challenges, we present $D^2$-MoE, a new delta decompression compressor for reducing the parameters of MoE LLMs. Based on observations of expert diversity, we decompose their weights into a shared base weight and unique delta weights. Specifically, our method first merges each expert's weight into the base weight using the Fisher information matrix to capture shared components. Then, we compress delta weights through Singular Value Decomposition (SVD) by exploiting their low-rank properties.
Finally, we introduce a semi-dynamical structured pruning strategy for the base weights, combining static and dynamic redundancy analysis to achieve further parameter reduction while maintaining input adaptivity. In this way, our $D^2$-MoE successfully compacts MoE LLMs to high compression ratios without additional training. Extensive experiments highlight the superiority of our approach, with over 13\% performance gains than other compressors on Mixtral|Phi-3.5|DeepSeek|Qwen2 MoE LLMs at 40$\sim$60\% compression rates. Codes are available in https://github.com/lliai/D2MoE.
|
Mixture of Experts, Efficient Large Language Models, Delta Decompression, Structured Compression
|
In this paper, we present a novel approach to compressing Mixture of Experts LLMs based on delta decompression.
| 98
|
2502.17298
|
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] |
https://github.com/lliai/d2moe
|
3,030
|
MoE-SVD: Structured Mixture-of-Experts LLMs Compression via Singular Value Decomposition
|
https://openreview.net/forum?id=acJ3vdFljk
|
[
"Wei Li",
"Lujun Li",
"Hao Gu",
"You-Liang Huang",
"Mark G. Lee",
"Shengjie Sun",
"Wei Xue",
"Yike Guo"
] |
Poster
|
Mixture of Experts (MoE) architecture improves Large Language Models (LLMs) with better scaling, but its higher parameter counts and memory demands create challenges for deployment. In this paper, we present MoE-SVD, a new decomposition-based compression framework tailored for MoE LLMs without any extra training. By harnessing the power of Singular Value Decomposition (SVD), MoE-SVD addresses the critical issues of decomposition collapse and matrix redundancy in MoE architectures. Specifically, we first decompose experts into compact low-rank matrices, resulting in accelerated inference and memory optimization. In particular, we propose selective decomposition strategy by measuring sensitivity metrics based on weight singular values and activation statistics to automatically identify decomposable expert layers. Then, we share a single V-matrix across all experts and employ a top-k selection for U-matrices. This low-rank matrix sharing and trimming scheme allows for significant parameter reduction while preserving diversity among experts. Comprehensive experiments on Mixtral, Phi-3.5, DeepSeek, and Qwen2 MoE LLMs show MoE-SVD outperforms other compression methods, achieving a 60\% compression ratio and 1.5× faster inference with minimal performance loss. Codes are available at: https://github.com/lliai/MoE-SVD.
|
Mixture of Experts, Efficient Large Language Models, Low-Rank Decomposition, Network Sparsity
|
In this paper, we present a novel training-free compressor for MoE LLMs that uses SVD to break experts into smaller matrices, sharing and trimming them to save memory, speed up inference.
| 95
| null |
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] | null |
3,031
|
SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
|
https://openreview.net/forum?id=anSWDvJm8v
|
[
"Haoquan Fang",
"Markus Grotz",
"Wilbert Pumacay",
"Yi Ru Wang",
"Dieter Fox",
"Ranjay Krishna",
"Jiafei Duan"
] |
Poster
|
Robotic manipulation systems operating in diverse, dynamic environments must exhibit three critical abilities: multitask interaction, generalization to unseen scenarios, and spatial memory. While significant progress has been made in robotic manipulation, existing approaches often fall short in generalization to complex environmental variations and addressing memory-dependent tasks. To bridge this gap, we introduce **SAM2Act**, a multi-view robotic transformer-based policy that leverages multi-resolution upsampling with visual representations from large-scale foundation model. SAM2Act achieves a state-of-the-art average success rate of **86.8% across 18 tasks** in the RLBench benchmark, and demonstrates robust generalization on *The Colosseum* benchmark, with only a **4.3% performance gap** under diverse environmental perturbations. Building on this foundation, we propose **SAM2Act+**, a memory-based architecture inspired by SAM2, which incorporates a memory bank, an encoder, and an attention mechanism to enhance spatial memory. To address the need for evaluating memory-dependent tasks, we introduce ***MemoryBench***, a novel benchmark designed to assess spatial memory and action recall in robotic manipulation. SAM2Act+ achieves an average success rate of **94.3% on memory-based tasks** in *MemoryBench*, significantly outperforming existing approaches and pushing the boundaries of memory-based robotic systems.
Project page: [sam2act.github.io](https://sam2act.github.io/).
|
Robot Learning, Behavior Cloning, Imitation Learning, Memory-based Robotics Transformer
|
Memory-based Multi View Robotics Transformer
| 93
|
2501.18564
|
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] |
https://github.com/sam2act/sam2act
|
3,032
|
COExpander: Adaptive Solution Expansion for Combinatorial Optimization
|
https://openreview.net/forum?id=KMaBXMWsBM
|
[
"Jiale Ma",
"Wenzheng Pan",
"Yang Li",
"Junchi Yan"
] |
Poster
|
Despite rapid progress in neural combinatorial optimization (NCO) for solving CO problems (COPs), as the problem scale grows, several bottlenecks persist: 1) solvers in the Global Prediction (GP) paradigm struggle in long-range decisions where the overly smooth intermediate heatmaps impede effective decoding, and 2) solvers in the Local Construction (LC) paradigm are time-consuming and incapable of tackling large instances due to the onerous auto-regressive process. Observing these challenges, we propose a new paradigm named Adaptive Expansion AE with its instantiation COExpander, positioned to leverage both advantages of GP and LC. COExpander utilizes informative heatmaps generated by a global predictor, which is learned under the guidance of locally determined partial solutions, to in turn direct the expansion of determined decision variables with adaptive step-sizes. To ensure transparent evaluation, we further take the lead to canonicalize 29 benchmarks spanning 6 popular COPs (MIS, MCl, MVC, MCut, TSP, ATSP) and various scales (50-10K nodes), upon which experiments demonstrate concrete SOTA performance of COExpander over these tasks. Source code and our standardized datasets will be made public.
|
Neural Combinatorial Optimization, Generative Modeling
| null | 79
| null |
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] |
https://github.com/Thinklab-SJTU/COExpander
|
3,033
|
BECAME: Bayesian Continual Learning with Adaptive Model Merging
|
https://openreview.net/forum?id=gU0MwTihsn
|
[
"Mei Li",
"Yuxiang Lu",
"Qinyan Dai",
"Suizhi Huang",
"Yue Ding",
"Hongtao Lu"
] |
Poster
|
Continual Learning (CL) strives to learn incrementally across tasks while mitigating catastrophic forgetting. A key challenge in CL is balancing stability (retaining prior knowledge) and plasticity (learning new tasks). While representative gradient projection methods ensure stability, they often limit plasticity. Model merging techniques offer promising solutions, but prior methods typically rely on empirical assumptions and carefully selected hyperparameters. In this paper, we explore the potential of model merging to enhance the stability-plasticity trade-off, providing theoretical insights that underscore its benefits. Specifically, we reformulate the merging mechanism using Bayesian continual learning principles and derive a closed-form solution for the optimal merging coefficient that adapts to the diverse characteristics of tasks. To validate our approach, we introduce a two-stage framework named BECAME, which synergizes the expertise of gradient projection and adaptive merging. Extensive experiments show that our approach outperforms state-of-the-art CL methods and existing merging strategies https://github.com/limei0818/BECAME.
|
Continual Learning, Incremental Learning, Model Merging
|
We provide therotical insights into model merging in continual learning and propose the optimal solution for adaptive merging.
| 70
|
2504.02666
|
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] |
https://github.com/limei0818/BECAME
|
3,034
|
Targeted control of fast prototyping through domain-specific interface
|
https://openreview.net/forum?id=6PVUY0FhbG
|
[
"Yu-Zhe Shi",
"Mingchen Liu",
"Hanlu Ma",
"Qiao Xu",
"Huamin Qu",
"Kun He",
"Lecheng Ruan",
"Qining Wang"
] |
Poster
|
Industrial designers have long sought a natural and intuitive way to achieve the targeted control of prototype models---using simple natural language instructions to configure and adjust the models seamlessly according to their intentions, without relying on complex modeling commands. While Large Language Models have shown promise in this area, their potential for controlling prototype models through language remains partially underutilized. This limitation stems from gaps between designers' languages and modeling languages, including mismatch in abstraction levels, fluctuation in semantic precision, and divergence in lexical scopes. To bridge these gaps, we propose an interface architecture that serves as a medium between the two languages. Grounded in design principles derived from a systematic investigation of fast prototyping practices, we devise the interface's operational mechanism and develop an algorithm for its automated domain specification. Both machine-based evaluations and human studies on fast prototyping across various product design domains demonstrate the interface's potential to function as an auxiliary module for Large Language Models, enabling precise and effective targeted control of prototype models.
|
Fast Prototyping, Domain-Specific Representation, Human Language Specification, Smart Manufacturing
| null | 61
|
2506.11070
|
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] | null |
3,035
|
CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization
|
https://openreview.net/forum?id=bxHgOYSQNe
|
[
"Dasol Hong",
"Wooju Lee",
"Hyun Myung"
] |
Poster
|
Prompt tuning, which adapts vision-language models by freezing model parameters and opti- mizing only the prompt, has proven effective for task-specific adaptations. The core challenge in prompt tuning is improving specialization for a specific task and generalization for unseen domains. However, frozen encoders often produce misaligned features, leading to confusion between classes and limiting specialization. To overcome this issue, we propose a confusion-aware loss (CoA-loss) that improves specialization by refining the decision boundaries between confusing classes. Additionally, we mathematically demonstrate that a mixture model can enhance generalization without compromising specialization. This is achieved using confidence-aware weights (CoA- weights), which adjust the weights of each prediction in the mixture model based on its confidence within the class domains. Extensive experiments show that CoCoA-Mix, a mixture model with CoA-loss and CoA-weights, outperforms state-of-the-art methods by enhancing specialization and generalization. Our code is publicly available at https://github.com/url-kaist/CoCoA-Mix
|
Context optimization, Prompt tuning, Vision-language models
|
CoCoA-Mix enhances specialization and generalization in prompt tuning using CoA-loss for refined decision boundaries and CoA-temp for confidence-based scaling.
| 47
| null |
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] |
https://github.com/url-kaist/CoCoA-Mix
|
3,036
|
Vision Graph Prompting via Semantic Low-Rank Decomposition
|
https://openreview.net/forum?id=rsegDeiPt9
|
[
"Zixiang Ai",
"Zichen Liu",
"Jiahuan Zhou"
] |
Poster
|
Vision GNN (ViG) demonstrates superior performance by representing images as graph structures, providing a more natural way to capture irregular semantic patterns beyond traditional grid or sequence-based representations. To efficiently adapt ViG to downstream tasks, parameter-efficient fine-tuning techniques like visual prompting become increasingly essential. However, existing prompting methods are primarily designed for Transformer-based models, neglecting the rich topological relationships among nodes and edges in graph-based representations, limiting their capacity to model complex semantics. In this paper, we propose Vision Graph Prompting (VGP), a novel framework tailored for vision graph structures. Our core insight reveals that semantically connected components in the graph exhibit low-rank properties.
Building on this observation, we introduce a semantic low-rank prompting method that decomposes low-rank semantic features and integrates them with prompts on vision graph topologies, capturing both global structural patterns and fine-grained semantic dependencies.
Extensive experiments demonstrate our method significantly improves ViG’s transfer performance on diverse downstream tasks, achieving results comparable to full fine-tuning while maintaining parameter efficiency.
|
Graph Neural Networks, Visual Prompting
|
In this paper, we propose Vision Graph Prompting (VGP), a novel framework tailored for vision graph structures.
| 40
|
2505.04121
|
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] |
https://github.com/zhoujiahuan1991/ICML2025-VGP
|
3,037
|
GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model
|
https://openreview.net/forum?id=4SsNofUQf1
|
[
"Zixiang Ai",
"Zichen Liu",
"Yuanhang Lei",
"Zhenyu Cui",
"Xu Zou",
"Jiahuan Zhou"
] |
Poster
|
Pre-trained 3D vision models have gained significant attention for their promising performance on point cloud data. However, fully fine-tuning these models for downstream tasks is computationally expensive and storage-intensive. Existing parameter-efficient fine-tuning (PEFT) approaches, which focus primarily on input token prompting, struggle to achieve competitive performance due to their limited ability to capture the geometric information inherent in point clouds. To address this challenge, we propose a novel Geometry-Aware Point Cloud Prompt (GAPrompt) that leverages geometric cues to enhance the adaptability of 3D vision models. First, we introduce a Point Prompt that serves as an auxiliary input alongside the original point cloud, explicitly guiding the model to capture fine-grained geometric details. Additionally, we present a Point Shift Prompter designed to extract global shape information from the point cloud, enabling instance-specific geometric adjustments at the input level. Moreover, our proposed Prompt Propagation mechanism incorporates the shape information into the model's feature extraction process, further strengthening its ability to capture essential geometric characteristics. Extensive experiments demonstrate that GAPrompt significantly outperforms state-of-the-art PEFT methods and achieves competitive results compared to full fine-tuning on various benchmarks, while utilizing only 2.19\% of trainable parameters.
|
Point Cloud Model, Parameter-Efficient Fine-tuning, Prompt Learning
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We propose a novel Geometry-Aware Point Cloud Prompt that leverages geometric cues to enhance the adaptability of 3D vision models.
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2505.04119
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] |
https://github.com/zhoujiahuan1991/ICML2025-GAPrompt
|
3,038
|
Token Coordinated Prompt Attention is Needed for Visual Prompting
|
https://openreview.net/forum?id=OhgLhXgr27
|
[
"Zichen Liu",
"Xu Zou",
"Gang Hua",
"Jiahuan Zhou"
] |
Poster
|
Visual prompting techniques are widely used to efficiently fine-tune pretrained Vision Transformers (ViT) by learning a small set of shared prompts for all tokens. However, existing methods overlook the unique roles of different tokens in conveying discriminative information and interact with all tokens using the same prompts, thereby limiting the representational capacity of ViT. This often leads to indistinguishable and biased prompt-extracted features, hindering performance. To address this issue, we propose a plug-and-play Token Coordinated Prompt Attention (TCPA) module, which assigns specific coordinated prompts to different tokens for attention-based interactions. Firstly, recognizing the distinct functions of CLS and image tokens-global information aggregation and local feature extraction, we disentangle the prompts into CLS Prompts and Image Prompts, which interact exclusively with CLS tokens and image tokens through attention mechanisms. This enhances their respective discriminative abilities. Furthermore, as different image tokens correspond to distinct image patches and contain diverse information, we employ a matching function to automatically assign coordinated prompts to individual tokens. This enables more precise attention interactions, improving the diversity and representational capacity of the extracted features. Extensive experiments across various benchmarks demonstrate that TCPA significantly enhances the diversity and discriminative power of the extracted features.
|
Visual Prompting, Prompt Learning
| null | 35
|
2505.02406
|
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] |
https://github.com/zhoujiahuan1991/ICML2025-TCPA
|
3,039
|
Componential Prompt-Knowledge Alignment for Domain Incremental Learning
|
https://openreview.net/forum?id=QWCdBzLOsk
|
[
"Kunlun Xu",
"Xu Zou",
"Gang Hua",
"Jiahuan Zhou"
] |
Poster
|
Domain Incremental Learning (DIL) aims to learn from non-stationary data streams across domains while retaining and utilizing past knowledge. Although prompt-based methods effectively store multi-domain knowledge in prompt parameters and obtain advanced performance through cross-domain prompt fusion, we reveal an intrinsic limitation: component-wise misalignment between domain-specific prompts leads to conflicting knowledge integration and degraded predictions. This arises from the random positioning of knowledge components within prompts, where irrelevant component fusion introduces interference.
To address this, we propose Componential Prompt-Knowledge Alignment (KA-Prompt), a novel prompt-based DIL method that introduces component-aware prompt-knowledge alignment during training, significantly improving both the learning and inference capacity of the model. KA-Prompt operates in two phases: (1) Initial Componential Structure Configuring, where a set of old prompts containing knowledge relevant to the new domain are mined via greedy search, which is then exploited to initialize new prompts to achieve reusable knowledge transfer and establish intrinsic alignment between new and old prompts. (2) Online Alignment Preservation, which dynamically identifies the target old prompts and applies adaptive componential consistency constraints as new prompts evolve. Extensive experiments on DIL benchmarks demonstrate the effectiveness of our KA-Prompt.
Our source code is available at https://github.com/zhoujiahuan1991/ICML2025-KA-Prompt.
|
Domain Incremental learning; Image Classification
| null | 31
|
2505.04575
|
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] |
https://github.com/zhoujiahuan1991/ICML2025-KA-Prompt
|
3,040
|
The Emperor's New Clothes in Benchmarking? A Rigorous Examination of Mitigation Strategies for LLM Benchmark Data Contamination
|
https://openreview.net/forum?id=TuvDxubEfE
|
[
"Yifan Sun",
"Han Wang",
"Dongbai Li",
"Gang Wang",
"Huan Zhang"
] |
Poster
|
Benchmark Data Contamination (BDC)—the inclusion of benchmark testing samples in the training set—has raised increasing concerns in Large Language Model (LLM) evaluation, leading to falsely inflated performance estimates and undermining evaluation reliability. To address this, researchers have proposed various mitigation strategies to update existing benchmarks, including modifying original questions or generating new ones based on them. However, a rigorous examination of the effectiveness of these mitigation strategies remains lacking. In this paper, we design a systematic and controlled pipeline along with two novel metrics—*fidelity* and *contamination resistance*—to provide a fine-grained and comprehensive assessment of existing BDC mitigation strategies. Previous assessment methods, such as accuracy drop and accuracy matching, focus solely on aggregate accuracy, often leading to incomplete or misleading conclusions. Our metrics address this limitation by emphasizing *question-level* evaluation result matching. Extensive experiments with 10 LLMs, 5 benchmarks, 20 BDC mitigation strategies, and 2 contamination scenarios reveal that no existing strategy effectively balances fidelity and contamination resistance. No semantic-preserving strategy yields a significant improvement in resistance over the vanilla case (*i.e.*, no benchmark update) across *all* benchmarks, while semantic-altering strategies sacrifice fidelity for resistance. These findings underscore the urgent need for designing more effective BDC mitigation strategies. Our code repository is available at https://github.com/ASTRAL-Group/BDC_mitigation_assessment.
|
Data Contamination, Large Language Models, Evaluation
|
We design a systematic and controlled pipeline along with two novel metrics, fidelity and resistance, to provide a fine-grained and comprehensive assessment of existing Benchmark Data Contamination mitigation strategies for Large Language Models.
| 25
|
2503.16402
|
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-0.0022709739860147238,
-0.035541847348213196,
-0.018693657591938972,
-0.030024133622646332,
0.003713039681315422,
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0.023944078013300896,
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] |
https://github.com/ASTRAL-Group/BDC_mitigation_assessment
|
3,041
|
How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects
|
https://openreview.net/forum?id=o2waQ5S2ty
|
[
"Wonkwang Lee",
"Jongwon Jeong",
"Taehong Moon",
"Hyeon-Jong Kim",
"Jaehyeon Kim",
"Gunhee Kim",
"Byeong-Uk Lee"
] |
Poster
|
Motion synthesis for diverse object categories holds great potential for 3D content creation but remains underexplored due to two key challenges: (1) the lack of comprehensive motion datasets that include a wide range of high-quality motions and annotations, and (2) the absence of methods capable of handling heterogeneous skeletal templates from diverse objects.
To address these challenges, we contribute the following:
First, we augment the Truebones Zoo dataset—a high-quality animal motion dataset covering over 70 species—by annotating it with detailed text descriptions, making it suitable for text-based motion synthesis.
Second, we introduce rig augmentation techniques that generate diverse motion data while preserving consistent dynamics, enabling models to adapt to various skeletal configurations.
Finally, we redesign existing motion diffusion models to dynamically adapt to arbitrary skeletal templates, enabling motion synthesis for a diverse range of objects with varying structures.
Experiments show that our method learns to generate high-fidelity motions from textual descriptions for diverse and even unseen objects, setting a strong foundation for motion synthesis across diverse object categories and skeletal templates.
Qualitative results are available on this [link](https://t2m4lvo.github.io).
|
motion synthesis for diverse objects, motion synthesis for large-vocabulary objects, text-to-motion synthesis, animal motion synthesis, non-human motion synthesis
|
In this work, we address text-to-motion synthesis for a wide range of object categories, including bipedal, quadrupedal, and winged animals, as well as fish and insects.
| 24
|
2503.04257
|
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] | null |
3,042
|
Teaching Physical Awareness to LLMs through Sounds
|
https://openreview.net/forum?id=Aycl60Rhbt
|
[
"Weiguo Wang",
"Andy Nie",
"Wenrui Zhou",
"Yi Kai",
"Chengchen Hu"
] |
Poster
|
Large Language Models (LLMs) have shown remarkable capabilities in text and multimodal processing, yet they fundamentally lack physical awareness--understanding of real-world physical phenomena.
In this work, we present ACORN, a framework that teaches LLMs physical awareness through sound, focusing on fundamental physical phenomena like the Doppler effect, multipath effect, and spatial relationships. To overcome data scarcity, ACORN introduce a physics-based simulator combining real-world sound sources with controlled physical channels to generate diverse training data. Using this simulator, we build AQA-PHY, a comprehensive Audio Question-Answer dataset, and propose an audio encoder that processes both magnitude and phase information. By connecting our audio encoder to state-of-the-art LLMs, we demonstrate reasonable results in both simulated and real-world tasks, such as line-of-sight detection, Doppler effect estimation, and Direction-of-Arrival estimation, paving the way for enabling LLMs to understand physical world.
|
LLM, Physical World Understanding, Audio, Spatial Audio, Acoustic Channel Simulation
|
This paper explores and validates the feasibility of teaching LLMs to understand the physical world through sounds
| 11
|
2506.08524
|
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] | null |
3,043
|
Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel Images
|
https://openreview.net/forum?id=BtRn4eayfE
|
[
"Zhengrui Guo",
"Qichen Sun",
"Jiabo MA",
"Lishuang Feng",
"Jinzhuo Wang",
"Hao Chen"
] |
Poster
|
Whole slide image (WSI) analysis presents significant computational challenges due to the massive number of patches in gigapixel images. While transformer architectures excel at modeling long-range correlations through self-attention, their quadratic computational complexity makes them impractical for computational pathology applications. Existing solutions like local-global or linear self-attention reduce computational costs but compromise the strong modeling capabilities of full self-attention. In this work, we propose **Querent**, *i.e.*, the **quer**y-awar**e** long co**nt**extual dynamic modeling framework, which achieves a theoretically bounded approximation of full self-attention while delivering practical efficiency. Our method adaptively predicts which surrounding regions are most relevant for each patch, enabling focused yet unrestricted attention computation only with potentially important contexts. By using efficient region-wise metadata computation and importance estimation, our approach dramatically reduces computational overhead while preserving global perception to model fine-grained patch correlations. Through comprehensive experiments on biomarker prediction, gene mutation prediction, cancer subtyping, and survival analysis across over 10 WSI datasets, our method demonstrates superior performance compared to the state-of-the-art approaches. Codes are available at https://github.com/dddavid4real/Querent.
|
Computational Pathology, Whole Slide Image, Cancer Diagnosis and Prognosis
| null | 10
|
2501.18984
|
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] |
https://github.com/dddavid4real/Querent
|
3,044
|
Sub-Sequential Physics-Informed Learning with State Space Model
|
https://openreview.net/forum?id=V7VnjJxBlg
|
[
"Chenhui Xu",
"Dancheng Liu",
"Yuting Hu",
"Jiajie Li",
"Ruiyang Qin",
"Qingxiao Zheng",
"Jinjun Xiong"
] |
Poster
|
Physics-Informed Neural Networks (PINNs) are a kind of deep-learning-based numerical solvers for partial differential equations (PDEs). Existing PINNs often suffer from failure modes of being unable to propagate patterns of initial conditions. We discover that these failure modes are caused by the simplicity bias of neural networks and the mismatch between PDE's continuity and PINN's discrete sampling. We reveal that the State Space Model (SSM) can be a continuous-discrete articulation allowing initial condition propagation, and that simplicity bias can be eliminated by aligning a sequence of moderate granularity. Accordingly, we propose PINNMamba, a novel framework that introduces sub-sequence modeling with SSM. Experimental results show that PINNMamba can reduce errors by up to 86.3\% compared with state-of-the-art architecture. Our code is available at Supplementary Material.
|
Physics-Informed Neural Networks, State Space Model
|
This paper introduces PINNMamba, a novel state-space-based sub-sequence learning framework for physics-informed neural networks.
| 7
|
2502.00318
|
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] |
https://github.com/miniHuiHui/PINNMamba
|
3,045
|
Policy Filtration for RLHF to Mitigate Noise in Reward Models
|
https://openreview.net/forum?id=L8hYdTQVcs
|
[
"Chuheng Zhang",
"Wei Shen",
"Li Zhao",
"Xuyun Zhang",
"Xiaolong Xu",
"Wanchun Dou",
"Jiang Bian"
] |
Poster
|
While direct policy optimization methods exist, pioneering LLMs are fine-tuned with reinforcement learning from human feedback (RLHF) to generate better responses under the supervision of a reward model learned from preference data. One major challenge of RLHF is the inaccuracy of the intermediate reward model, especially in the tasks that requires complex reasoning for the reward model to score a response. We find that the reliability of the reward model varies across responses assigned with different rewards. This motivates us to filter the samples whose rewards may be unreliable to improve the signal-to-noise ratio during policy learning, resulting in Policy Filtration for Proximal Policy Optimization (PF-PPO). To choose a proper policy filtering strategy, we use the coefficient of determination ($R^2$) between the rewards and actual scores on filtered samples as the metrics to help us find promising strategies since it measures how well the rewards filtered by PF-PPO indicate real performance. We provide extensive experiments to validate the effectiveness of PF-PPO in code generation and math reasoning tasks. In code generation, PF-PPO achieves the state-of-the-art performance of 7-billion-parameter models on HumanEval (+7.9%), MBPP (+0.7%), and LeetCode Contest (+10.0%) which is a more challenging benchmark created by us. In math reasoning, PF-PPO yields performance increase using different reward models and benchmarks (Ape210K and CMATH).
|
AI, LLM, RLHF
|
We design a method to filter samples during RLHF to mitigate the inaccuracy of reward model.
| 1
|
2409.06957
|
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] |
https://github.com/swtheing/PF-PPO-RLHF
|
3,046
|
Towards Practical Defect-Focused Automated Code Review
|
https://openreview.net/forum?id=mEV0nvHcK3
|
[
"Junyi Lu",
"Lili Jiang",
"Xiaojia Li",
"Jianbing Fang",
"Fengjun Zhang",
"Li Yang",
"Chun Zuo"
] |
Spotlight
|
The complexity of code reviews has driven efforts to automate review comments, but prior approaches oversimplify this task by treating it as snippet-level code-to-text generation and relying on text similarity metrics like BLEU for evaluation. These methods overlook repository context, real-world merge request evaluation, and defect detection, limiting their practicality. To address these issues, we explore the full automation pipeline within the online recommendation service of a company with nearly 400 million daily active users, analyzing industry-grade C++ codebases comprising hundreds of thousands of lines of code. We identify four key challenges: 1) capturing relevant context, 2) improving key bug inclusion (KBI), 3) reducing false alarm rates (FAR), and 4) integrating human workflows. To tackle these, we propose 1) code slicing algorithms for context extraction, 2) a multi-role LLM framework for KBI, 3) a filtering mechanism for FAR reduction, and 4) a novel prompt design for better human interaction. Our approach, validated on real-world merge requests from historical fault reports, achieves a 2× improvement over standard LLMs and a 10× gain over previous baselines. While the presented results focus on C++, the underlying framework design leverages language-agnostic principles (e.g., AST-based analysis), suggesting potential for broader applicability.
|
Automated Code Review, Merge Request Analysis, Large Language Models (LLMs), Defect Detection, Evaluation Metrics for Code Review, Code Context Extraction, Multi-Agent LLM Collaboration
|
This work presents an end-to-end approach to automated code review that goes beyond snippet-level generation and text-similarity metrics, achieving significant gains over existing baselines in real-world, industry-scale codebases.
| 16,368
|
2505.17928
|
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] | null |
3,047
|
BaxBench: Can LLMs Generate Correct and Secure Backends?
|
https://openreview.net/forum?id=il3KRr4H9u
|
[
"Mark Vero",
"Niels Mündler",
"Victor Chibotaru",
"Veselin Raychev",
"Maximilian Baader",
"Nikola Jovanović",
"Jingxuan He",
"Martin Vechev"
] |
Spotlight
|
Automatic program generation has long been a fundamental challenge in computer science. Recent benchmarks have shown that large language models (LLMs) can effectively generate code at the function level, make code edits, and solve algorithmic coding tasks. However, to achieve full automation, LLMs should be able to generate production-quality, self-contained application modules. To evaluate the capabilities of LLMs in solving this challenge, we introduce BaxBench, a novel evaluation benchmark consisting of 392 tasks for the generation of backend applications. We focus on backends for three critical reasons: (i) they are practically relevant, building the core components of most modern web and cloud software, (ii) they are difficult to get right, requiring multiple functions and files to achieve the desired functionality, and (iii) they are security-critical, as they are exposed to untrusted third-parties, making secure solutions that prevent deployment-time attacks an imperative. BaxBench validates the functionality of the generated applications with comprehensive test cases, and assesses their security exposure by executing end-to-end exploits. Our experiments reveal key limitations of current LLMs in both functionality and security: (i) even the best model, OpenAI o1, achieves a mere 62% on code correctness; (ii) on average, we could successfully execute security exploits on around half of the correct programs generated by each LLM; and (iii) in less popular backend frameworks, models further struggle to generate correct and secure applications. Progress on BaxBench signifies important steps towards autonomous and secure software development with LLMs.
|
large language model, large language models, LLM, code generation, code security, security, benchmark
|
This paper introduces a novel benchmark to measure the correctness and security of LLM-generated code for backend applications.
| 16,211
|
2502.11844
|
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] |
https://github.com/logic-star-ai/baxbench
|
3,048
|
Hide & Seek: Transformer Symmetries Obscure Sharpness & Riemannian Geometry Finds It
|
https://openreview.net/forum?id=5QAKPBVdFH
|
[
"Marvin F. da Silva",
"Felix Dangel",
"Sageev Oore"
] |
Spotlight
|
The concept of sharpness has been successfully applied to traditional architectures like MLPs and CNNs to predict their generalization. For transformers, however, recent work reported weak correlation between flatness and generalization. We argue that existing sharpness measures fail for transformers, because they have much richer symmetries in their attention mechanism that induce directions in parameter space along which the network or its loss remain identical. We posit that sharpness must account fully for these symmetries, and thus we redefine it on a quotient manifold that results from quotienting out the transformer symmetries, thereby removing their ambiguities. Leveraging tools from Riemannian geometry, we propose a fully general notion of sharpness, in terms of a geodesic ball on the symmetry-corrected quotient manifold. In practice, we need to resort to approximating the geodesics. Doing so up to first order yields existing adaptive sharpness measures, and we demonstrate that including higher-order terms is crucial to recover correlation with generalization. We present results on diagonal networks with synthetic data, and show that our geodesic sharpness reveals strong correlation for real-world transformers on both text and image classification tasks.
|
generalization, symmetry, sharpness, flatness, riemannian geometry, loss landscape
| null | 15,939
|
2505.05409
|
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] | null |
3,049
|
Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream
|
https://openreview.net/forum?id=WxY61MmHYo
|
[
"Abdulkadir Gokce",
"Martin Schrimpf"
] |
Spotlight
|
When trained on large-scale object classification datasets, certain artificial neural network models begin to approximate core object recognition behaviors and neural response patterns in the primate brain. While recent machine learning advances suggest that scaling compute, model size, and dataset size improves task performance, the impact of scaling on brain alignment remains unclear. In this study, we explore scaling laws for modeling the primate visual ventral stream by systematically evaluating over 600 models trained under controlled conditions on benchmarks spanning V1, V2, V4, IT and behavior. We find that while behavioral alignment continues to scale with larger models, neural alignment saturates. This observation remains true across model architectures and training datasets, even though models with stronger inductive biases and datasets with higher-quality images are more compute-efficient. Increased scaling is especially beneficial for higher-level visual areas, where small models trained on few samples exhibit only poor alignment. Our results suggest that while scaling current architectures and datasets might suffice for alignment with human core object recognition behavior, it will not yield improved models of the brain's visual ventral stream, highlighting the need for novel strategies in building brain models.
|
scaling laws, neural alignment, behavioral alignment, computer vision, primate visual ventral stream
|
We systematically explored scaling laws for primate vision models and discovered that neural alignment stops improving beyond a certain scale, even though behavior keeps aligning better.
| 15,849
|
2411.05712
|
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] |
https://github.com/epflneuroailab/scaling-primate-vvs
|
3,050
|
Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes
|
https://openreview.net/forum?id=EW2JR5aVLm
|
[
"Dongjae Jeon",
"Dueun Kim",
"Albert No"
] |
Spotlight
|
In this paper, we introduce a geometric framework to analyze memorization in diffusion models through the sharpness of the log probability density. We mathematically justify a previously proposed score-difference-based memorization metric by demonstrating its effectiveness in quantifying sharpness. Additionally, we propose a novel memorization metric that captures sharpness at the initial stage of image generation in latent diffusion models, offering early insights into potential memorization. Leveraging this metric, we develop a mitigation strategy that optimizes the initial noise of the generation process using a sharpness-aware regularization term.
|
Diffusion Model, Memorization, Sharpness Aware
|
We propose a sharpness-based framework to analyze and mitigate memorization in diffusion models through novel metrics and initial point adjustment strategies.
| 15,448
| null |
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] | null |
3,051
|
Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport
|
https://openreview.net/forum?id=DUGFTH9W8B
|
[
"Tuan Quang Dam",
"Pascal Stenger",
"Lukas Schneider",
"Joni Pajarinen",
"Carlo D'Eramo",
"Odalric-Ambrym Maillard"
] |
Spotlight
|
This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS) tailored for highly stochastic and partially observable Markov decision processes. We adopt a probabilistic approach, modeling both value and action-value nodes as Gaussian distributions, to introduce a novel backup operator that computes value nodes as the Wasserstein barycenter of their action-value children nodes; thus, propagating the uncertainty of the estimate across the tree to the root node. We study our novel backup operator when using a novel combination of $L^1$-Wasserstein barycenter with $\alpha$-divergence, by drawing a crucial connection to the generalized mean backup operator. We complement our probabilistic backup operator with two sampling strategies, based on optimistic selection and Thompson sampling, obtaining our Wasserstein MCTS algorithm. We provide theoretical guarantees of asymptotic convergence of $\mathcal{O}(n^{-1/2})$, with $n$ as the number of visited trajectories, to the optimal policy and an empirical evaluation on several stochastic and partially observable environments, where our approach outperforms well-known related baselines.
|
Monte-Carlo Tree Search, Planning under Uncertainty
|
We develop a new planning algorithm that handles uncertainty by tracking probability distributions instead of single values, achieving 20-80% better performance in unpredictable environments.
| 15,359
|
2309.10737
|
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] | null |
3,052
|
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
|
https://openreview.net/forum?id=qOgKMqv9T7
|
[
"Hyeongwon Jang",
"Changhun Kim",
"Eunho Yang"
] |
Spotlight
|
Recent explainable artificial intelligence (XAI) methods for time series primarily estimate point-wise attribution magnitudes, while overlooking the directional impact on predictions, leading to suboptimal identification of significant points. Our analysis shows that conventional Integrated Gradients (IG) effectively capture critical points with both positive and negative impacts on predictions. However, current evaluation metrics fail to assess this capability, as they inadvertently cancel out opposing feature contributions. To address this limitation, we propose novel evaluation metrics—Cumulative Prediction Difference (CPD) and Cumulative Prediction Preservation
(CPP)—to systematically assess whether attribution methods accurately identify significant positive and negative points in time series XAI. Under these metrics, conventional IG outperforms recent counterparts. However, directly applying IG to time series data may lead to suboptimal outcomes, as generated paths ignore temporal relationships and introduce out-of-distribution samples. To overcome these challenges, we introduce TIMING, which enhances IG by incorporating temporal awareness while maintaining its theoretical properties. Extensive experiments on synthetic and real-world time series benchmarks demonstrate that TIMING outperforms existing time
series XAI baselines. Our code is available at https://github.com/drumpt/TIMING.
|
Time Series, XAI, Explainability
| null | 15,165
|
2506.05035
|
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] |
https://github.com/drumpt/TIMING
|
3,053
|
Multi-Turn Code Generation Through Single-Step Rewards
|
https://openreview.net/forum?id=aJeLhLcsh0
|
[
"Arnav Kumar Jain",
"Gonzalo Gonzalez-Pumariega",
"Wayne Chen",
"Alexander M Rush",
"Wenting Zhao",
"Sanjiban Choudhury"
] |
Spotlight
|
We address the problem of code generation from multi-turn execution feedback.
Existing methods either generate code without feedback or use complex, hierarchical reinforcement learning to optimize multi-turn rewards.
We propose a simple yet scalable approach, $\mu$CODE, that solves multi-turn code generation using only single-step rewards.
Our key insight is that code generation is a one-step recoverable MDP, where the correct code can be recovered from any intermediate code state in a single turn.
$\mu$CODE iteratively trains both a generator to provide code solutions conditioned on multi-turn execution feedback and a verifier to score the newly generated code.
Experimental evaluations show that our approach achieves significant improvements over state-of-the-art baselines.
We provide analysis of the design choices of the reward models and policy, and show the efficacy of $\mu$CODE at utilizing the execution feedback.
|
Code Generation, Language Models, Reinforcement Learning
| null | 15,056
|
2502.20380
|
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] |
https://github.com/portal-cornell/muCode
|
3,054
|
Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
|
https://openreview.net/forum?id=4yHWV3B6g4
|
[
"Ulzee An",
"Moonseong Jeong",
"Simon Austin Lee",
"Aditya Gorla",
"Yuzhe Yang",
"Sriram Sankararaman"
] |
Spotlight
|
Current challenges in developing foundational models for volumetric imaging data, such as magnetic resonance imaging (MRI), stem from the computational complexity of state-of-the-art architectures in high dimensions and curating sufficiently large datasets of volumes.
To address these challenges, we introduce Raptor (Random Planar Tensor Reduction), a train-free method for generating semantically rich embeddings for volumetric data. Raptor leverages a frozen 2D foundation model, pretrained on natural images, to extract visual tokens from individual cross-sections of medical volumes. These tokens are then spatially compressed using random projections, significantly reducing computational complexity while retaining rich semantic information. Extensive experiments on 10 diverse medical volume tasks verify the superior performance of Raptor over state-of-the-art methods, including those pretrained exclusively on medical volumes (+3 SuPreM, +6 MISFM, +10 Merlin, +13 VoCo, and +14 SLIViT), while entirely bypassing the need for costly training. Our results highlight Raptor's effectiveness and versatility as a foundation for advancing deep learning-based methods for medical volumes (code: github.com/sriramlab/raptor).
|
Embedding, Volumes, Foundation model, Random projection, Compression, MRI, CT
|
Raptor (Random Planar Tensor Reduction) leverages a pretrained image model (DINOv2-L) to create and compress visual tokens in 3D, and achieves state-of-the-art performance across 10 medical volume tasks without the need for costly training.
| 15,029
|
2507.08254
|
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] |
https://github.com/sriramlab/raptor
|
3,055
|
The Role of Randomness in Stability
|
https://openreview.net/forum?id=W2Fe1hT7Ks
|
[
"Max Hopkins",
"Shay Moran"
] |
Spotlight
|
Stability is a central property in learning and statistics promising the output of an algorithm $\mathcal{A}$ does not change substantially when applied to similar datasets $S$ and $S'$. It is an elementary fact that any sufficiently stable algorithm (e.g.\ one returning the same result with high probability, satisfying privacy guarantees, etc.) must be randomized. This raises a natural question: can we quantify \textit{how much} randomness is needed for algorithmic stability? We study the randomness complexity of two influential notions of stability in learning: \textit{replicability} (which promises $\mathcal{A}$ usually outputs the same result when run over samples from the same distribution), and \textit{differential privacy} (which promises the output distribution of $\mathcal{A}$ remains similar under neighboring datasets). In particular, building on the ideas of (Dixon, Pavan, Vander Woude, and Vinodchandran ICML 2024) and (Cannone, Su, and Vadhan ITCS 2024), we prove a "weak-to-strong" boosting theorem for stability in these settings: the randomness complexity of a task $\mathcal{M}$ is tightly controlled by the best replication probability of any \textit{deterministic} algorithm solving $\mathcal{M}$, a parameter known as $\mathcal{M}$'s "global stability" (Chase, Moran, Yehudayoff FOCS 2023). Finally, we use this connection to characterize the randomness complexity of PAC Learning: a class has bounded randomness complexity iff it has finite Littlestone dimension, and moreover scales at worst logarithmically in the excess error of the learner. As a corollary, we resolve a question of (Chase, Chornomaz, Moran, and Yehudayoff STOC 2024) about the error-dependent list-replicability of agnostic learning.
|
Replicability, Differential Privacy, PAC Learning, Randomness, Stability
|
We study the number of random bits needed to achieve replicability and differential privacy for general statistical tasks and PAC Learning.
| 14,750
|
2502.08007
|
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] | null |
3,056
|
Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints
|
https://openreview.net/forum?id=a6Cagkpmgz
|
[
"Ruichuan Huang",
"Jiawei Zhang",
"Ahmet Alacaoglu"
] |
Spotlight
|
We propose smoothed primal-dual algorithms for solving stochastic nonconvex optimization problems with linear \emph{inequality} constraints. Our algorithms are single-loop and only require a single (or two) samples of stochastic gradients at each iteration. A defining feature of our algorithm is that it is based on an inexact gradient descent framework for the Moreau envelope, where the gradient of the Moreau envelope is estimated using one step of a stochastic primal-dual (linearized) augmented Lagrangian algorithm. To handle inequality constraints and stochasticity, we combine the recently established global error bounds in constrained optimization with a Moreau envelope-based analysis of stochastic proximal algorithms. We establish the optimal (in their respective cases) $O(\varepsilon^{-4})$ and $O(\varepsilon^{-3})$ sample complexity guarantees for our algorithms and provide extensions to stochastic linear constraints. Unlike existing methods, iterations of our algorithms are free of subproblems, large batch sizes or increasing penalty parameters in their iterations and they use dual variable updates to ensure feasibility.
|
primal-dual algorithms, stochastic optimization, single-loop algorithms, linear inequality constraints
|
We develop optimal single-loop primal-dual algorithms for stochastic nonconvex optimization with linear inequality constraints.
| 14,618
|
2504.07607
|
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] | null |
3,057
|
Masked Autoencoders Are Effective Tokenizers for Diffusion Models
|
https://openreview.net/forum?id=dzwUOiBlQW
|
[
"Hao Chen",
"Yujin Han",
"Fangyi Chen",
"Xiang Li",
"Yidong Wang",
"Jindong Wang",
"Ze Wang",
"Zicheng Liu",
"Difan Zou",
"Bhiksha Raj"
] |
Spotlight
|
Recent advances in latent diffusion models have demonstrated their effectiveness for high-resolution image synthesis. However, the properties of the latent space from tokenizer for better learning and generation of diffusion models remain under-explored. Theoretically and empirically, we find that improved generation quality is closely tied to the latent distributions with better structure, such as the ones with fewer Gaussian Mixture modes and more discriminative features. Motivated by these insights, we propose MAETok, an autoencoder (AE) leveraging mask modeling to learn semantically rich latent space while maintaining reconstruction fidelity.
Extensive experiments validate our analysis, demonstrating that the variational form of autoencoders is not necessary, and a discriminative latent space from AE alone enables state-of-the-art performance on ImageNet generation using only 128 tokens. MAETok achieves significant practical improvements, enabling a gFID of 1.69 with 76× faster training and 31× higher inference throughput for 512×512 generation. Our findings show that the structure of the latent space, rather than variational constraints, is crucial for effective diffusion models. Code and trained models will be released.
|
Diffusion Models, Autoencoders, Masked Autoencoders
| null | 14,576
|
2502.03444
|
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] |
https://github.com/Hhhhhhao/continuous_tokenizer
|
3,058
|
Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance
|
https://openreview.net/forum?id=w0xYx9CJhY
|
[
"Linxi Zhao",
"Yihe Deng",
"Weitong Zhang",
"Quanquan Gu"
] |
Spotlight
|
The advancement of Large Vision-Language Models (LVLMs) has increasingly highlighted the critical issue of their tendency to hallucinate non-existing objects in the images. To address this issue, previous works focused on using specially curated datasets or powerful LLMs to rectify the outputs of LVLMs. However, these approaches require either costly training or fine-tuning, or API access to proprietary LLMs for post-generation correction. In response to these limitations, we propose Mitigating hallucinAtion via image-gRounded guIdaNcE (MARINE), a framework that is both training-free and API-free. MARINE effectively and efficiently reduces object hallucinations during inference by introducing image-grounded guidance to LVLMs. This is achieved by leveraging open-source vision models to extract object-level information, thereby enhancing the precision of LVLM-generated content. Our framework's flexibility further allows for the integration of multiple vision models, enabling more reliable and robust object-level guidance. Through comprehensive evaluations across 5 popular LVLMs with diverse evaluation metrics and benchmarks, we demonstrate the effectiveness of MARINE, which even outperforms existing fine-tuning-based methods. Remarkably, it reduces hallucinations consistently in GPT-4V-assisted evaluation while maintaining the detailedness of LVLMs' generations. We release our code at https://github.com/Linxi-ZHAO/MARINE.
|
Large Vision-Language Models, Object Hallucination, Multi-modal LLMs
| null | 14,554
|
2402.08680
|
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] |
https://github.com/Linxi-ZHAO/MARINE
|
3,059
|
Policy-labeled Preference Learning: Is Preference Enough for RLHF?
|
https://openreview.net/forum?id=qLfo1sef50
|
[
"Taehyun Cho",
"Seokhun Ju",
"Seungyub Han",
"Dohyeong Kim",
"Kyungjae Lee",
"Jungwoo Lee"
] |
Spotlight
|
To design reward that align with human goals, Reinforcement Learning from Human Feedback (RLHF) has emerged as a prominent technique for learning reward functions from human preferences and optimizing models using reinforcement learning algorithms.
However, existing RLHF methods often misinterpret trajectories as being generated by an optimal policy, causing inaccurate likelihood estimation and suboptimal learning. To address this, we propose Policy-labeled Preference Learning (PPL) within the Direct Preference Optimization (DPO) framework, which resolves these likelihood mismatch problems by modeling human preferences with regret, reflecting the efficiency of executed policies. Additionally, we introduce a contrastive KL regularization term derived from regret-based principles to enhance sequential contrastive learning. Experiments in high-dimensional continuous control environments demonstrate PPL's significant improvements in offline RLHF performance and its effectiveness in online settings.
|
Reinforcement Learning from Human Feedback, Direct Preference Optimization, Regret Minimization
| null | 14,278
|
2505.06273
|
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] | null |
3,060
|
Generalized Random Forests Using Fixed-Point Trees
|
https://openreview.net/forum?id=1w0Zp99dnX
|
[
"David Fleischer",
"David A. Stephens",
"Archer Y. Yang"
] |
Spotlight
|
We propose a computationally efficient alternative to generalized random forests (GRFs) for estimating heterogeneous effects in large dimensions. While GRFs rely on a gradient-based splitting criterion, which in large dimensions is computationally expensive and unstable, our method introduces a fixed-point approximation that eliminates the need for Jacobian estimation. This gradient-free approach preserves GRF’s theoretical guarantees of consistency and asymptotic normality while significantly improving computational efficiency. We demonstrate that our method achieves a speedup of multiple times over standard GRFs without compromising statistical accuracy. Experiments on both simulated and real-world data validate our approach. Our findings suggest that the proposed method is a scalable alternative for localized effect estimation in machine learning and causal inference applications.
|
Generalized Random Forests, Fixed-Point Methods, Ensemble Methods, Causal Inference, Computational Efficiency
|
We introduce a fixed-point tree-growing algorithm that replaces the gradient-based splitting mechanism in GRFs, enabling faster and memory-efficient forest construction while preserving estimator accuracy.
| 14,206
|
2306.11908
|
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] |
https://github.com/dfleis/grf-experiments
|
3,061
|
On Learning Parallel Pancakes with Mostly Uniform Weights
|
https://openreview.net/forum?id=jNCTdUsQaC
|
[
"Ilias Diakonikolas",
"Daniel Kane",
"Sushrut Karmalkar",
"Jasper C.H. Lee",
"Thanasis Pittas"
] |
Spotlight
|
We study the complexity of learning $k$-mixtures of Gaussians ($k$-GMMs) on $\mathbb R^d$. This task is known to have complexity $d^{\Omega(k)}$ in full generality. To circumvent this exponential lower bound on the number of components, research has focused on learning families of GMMs satisfying additional structural properties. A natural assumption posits that the component weights are not exponentially small and that the components have the same unknown covariance. Recent work gave a $d^{O(\log(1/w_{\min}))}$-time algorithm for this class of GMMs, where $w_{\min}$ is the minimum weight. Our first main result is a Statistical Query (SQ) lower bound showing that this quasi-polynomial upper bound is essentially best possible, even for the special case of uniform weights. Specifically, we show that it is SQ-hard to distinguish between such a mixture and the standard Gaussian. We further explore how the distribution of weights affects the complexity of this task. Our second main result is a quasi-polynomial upper bound for the aforementioned testing task when most of the weights are uniform while a small fraction of the weights are potentially arbitrary.
|
gaussian mixture models, parallel pancakes, statistical query, high-dimensional statistics
|
We study the complexity of learning k-mixtures of Gaussians with shared covariance and non-trivial weights, establishing a lower bound matching existing algorithms and extending testing algorithms to handle more general weight distributions.
| 14,052
|
2504.15251
|
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] | null |
3,062
|
Monte Carlo Tree Diffusion for System 2 Planning
|
https://openreview.net/forum?id=XrCbBdycDc
|
[
"Jaesik Yoon",
"Hyeonseo Cho",
"Doojin Baek",
"Yoshua Bengio",
"Sungjin Ahn"
] |
Spotlight
|
Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)—whose performance naturally improves with inference-time computation scaling—standard diffusion‐based planners offer only limited avenues for the scalability. In this paper, we introduce Monte Carlo Tree Diffusion (MCTD), a novel framework that integrates the generative strength of diffusion models with the adaptive search capabilities of MCTS. Our method reconceptualizes denoising as a tree‐structured process, allowing partially denoised plans to be iteratively evaluated, pruned, and refined. By selectively expanding promising trajectories while retaining the flexibility to revisit and improve suboptimal branches, MCTD achieves the benefits of MCTS such as controlling exploration-exploitation trade-offs within the diffusion framework. Empirical results on challenging long‐horizon tasks show that MCTD outperforms diffusion baselines, yielding higher‐quality solutions as inference-time computation increases.
|
Diffusion, MCTS, Long-term Planning, Offline RL, Goal-conditioned RL, Inference-Time Scaling
|
Monte Carlo Tree Diffusion (MCTD) merges MCTS with diffusion-based partial denoising, enabling superior long-horizon planning by adaptively exploring and refining trajectories.
| 13,917
|
2502.07202
|
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] | null |
3,063
|
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents
|
https://openreview.net/forum?id=Ukjl86EsIk
|
[
"Shayan Kiyani",
"George J. Pappas",
"Aaron Roth",
"Hamed Hassani"
] |
Spotlight
|
A fundamental question in data-driven decision making is how to quantify the uncertainty of predictions to inform risk-sensitive downstream actions, as often required in domains such as medicine. We develop a decision-theoretic foundation linking prediction sets to risk-averse decision-making, addressing three questions: (1) What is the correct notion of uncertainty quantification for risk-averse decision makers? We prove that prediction sets are optimal for decision makers who wish to optimize their value at risk. (2) What is the optimal policy that a risk averse decision maker should use to map prediction sets to actions? We show that a simple max-min decision policy is optimal for risk-averse decision makers. Finally, (3) How can we derive prediction sets that are optimal for such decision makers? We provide an exact characterization in the population regime and a distribution free finite-sample construction. These insights leads to *Risk-Averse Calibration (RAC)*, a principled algorithm that is both *practical*—exploiting black-box predictions to enhance downstream utility—and *safe*—adhering to user-defined risk thresholds. We experimentally demonstrate RAC's advantages in medical diagnosis and recommendation systems, showing that it substantially improves the trade-off between safety and utility, delivering higher utility than existing methods while avoiding critical errors.
|
Decision making, Uncertainty Quantification, Prediction sets, Conformal prediction, Calibration, Risk Averse, Risk Aware, Risk sensitive, distribution free
| null | 13,862
|
2502.02561
|
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] |
https://github.com/shayankiyani98/Risk-Averse-Calibration
|
3,064
|
Algorithms with Calibrated Machine Learning Predictions
|
https://openreview.net/forum?id=Obet2x6GNl
|
[
"Judy Hanwen Shen",
"Ellen Vitercik",
"Anders Wikum"
] |
Spotlight
|
The field of *algorithms with predictions* incorporates machine learning advice in the design of online algorithms to improve real-world performance. A central consideration is the extent to which predictions can be trusted—while existing approaches often require users to specify an aggregate trust level, modern machine learning models can provide estimates of prediction-level uncertainty. In this paper, we propose *calibration* as a principled and practical tool to bridge this gap, demonstrating the benefits of calibrated advice through two case studies: the *ski rental* and *online job scheduling* problems. For ski rental, we design an algorithm that achieves near-optimal prediction-dependent performance and prove that, in high-variance settings, calibrated advice offers more effective guidance than alternative methods for uncertainty quantification. For job scheduling, we demonstrate that using a calibrated predictor leads to significant performance improvements over existing methods. Evaluations on real-world data validate our theoretical findings, highlighting the practical impact of calibration for algorithms with predictions.
|
calibration, algorithms with predictions, uncertainty quantification, ski rental, job scheduling
|
Algorithms with predictions does not assume anything about the quality of ML advice, but calibration guarantees are easy to achieve and enable the design of online algorithms with strong average performance.
| 13,817
|
2502.02861
|
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] |
https://github.com/heyyjudes/algs-cali-pred
|
3,065
|
PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling
|
https://openreview.net/forum?id=sEBfiF8JBu
|
[
"Avery Ma",
"Yangchen Pan",
"Amir-massoud Farahmand"
] |
Spotlight
|
Many-shot jailbreaking circumvents the safety alignment of LLMs by exploiting their ability to process long input sequences. To achieve this, the malicious target prompt is prefixed with hundreds of fabricated conversational exchanges between the user and the model. These exchanges are randomly sampled from a pool of unsafe question-answer pairs, making it appear as though the model has already complied with harmful instructions. In this paper, we present PANDAS: a hybrid technique that improves many-shot jailbreaking by modifying these fabricated dialogues with Positive Affirmations, Negative Demonstrations, and an optimized Adaptive Sampling method tailored to the target prompt's topic. We also introduce ManyHarm, a dataset of harmful question–answer pairs, and demonstrate through extensive experiments that PANDAS significantly outperforms baseline methods in long-context scenarios. Through attention analysis, we provide insights into how long-context vulnerabilities are exploited and show how PANDAS further improves upon many-shot jailbreaking.
|
jailbreaking, long-context LLMs, in-context learning
|
We propose PANDAS, a hybrid technique designed to improve jailbreaking success rate in long-context scenarios.
| 13,745
|
2502.01925
|
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] |
https://github.com/averyma/pandas
|
3,066
|
RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
|
https://openreview.net/forum?id=PzSG5nKe1q
|
[
"Jonas Gehring",
"Kunhao Zheng",
"Jade Copet",
"Vegard Mella",
"Taco Cohen",
"Gabriel Synnaeve"
] |
Spotlight
|
Large language models (LLMs) deployed as agents solve user-specified tasks over multiple steps while keeping the required manual engagement to a minimum. Crucially, such LLMs need to ground their generations in any feedback obtained to reliably achieve the desired outcomes. We propose an end-to-end reinforcement learning method for teaching models to leverage execution feedback in the realm of code synthesis, where state-of-the-art LLMs struggle to improve code iteratively compared to independent sampling. We benchmark on competitive programming tasks and achieve large performance gains with both small (8B parameters) and large (70B) models, outperforming previous work while reducing the number of samples required by an order of magnitude. Our analysis of inference-time behavior demonstrates that our method produces LLMs that effectively leverage automatic feedback over multiple steps.
|
large language models, multi-turn code generation, reinforcement learning, LLM agents
|
We apply reinforcement learning to ground LLMs in execution feedback for effective multi-turn code generation.
| 13,473
|
2410.02089
|
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] | null |
3,067
|
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
|
https://openreview.net/forum?id=M7mVzCV6uU
|
[
"Terje Mildner",
"Oliver Hamelijnck",
"Paris Giampouras",
"Theodoros Damoulas"
] |
Spotlight
|
We introduce FedGVI, a probabilistic Federated Learning (FL) framework that is robust to both prior and likelihood misspecification. FedGVI addresses limitations in both frequentist and Bayesian FL by providing unbiased predictions under model misspecification, with calibrated uncertainty quantification. Our approach generalises previous FL approaches, specifically Partitioned Variational Inference (Ashman et al., 2022), by allowing robust and conjugate updates, decreasing computational complexity at the clients. We offer theoretical analysis in terms of fixed-point convergence, optimality of the cavity distribution, and provable robustness to likelihood misspecification. Further, we empirically demonstrate the effectiveness of FedGVI in terms of improved robustness and predictive performance on multiple synthetic and real world classification data sets.
|
Federated Learning, Probabilistic Machine Learning, Model Misspecification, Robustness, Generalised Variational Inference
| null | 13,434
|
2502.00846
|
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] |
https://github.com/Terje-M/FedGVI
|
3,068
|
Stronger Neyman Regret Guarantees for Adaptive Experimental Design
|
https://openreview.net/forum?id=gcgzQSKR7y
|
[
"Georgy Noarov",
"Riccardo Fogliato",
"Martin Andres Bertran",
"Aaron Roth"
] |
Spotlight
|
We study the design of adaptive, sequential experiments for unbiased average treatment effect (ATE) estimation in the design-based potential outcomes setting. Our goal is to develop adaptive designs offering *sublinear Neyman regret*, meaning their efficiency must approach that of the hindsight-optimal nonadaptive design.
Recent work [Dai et al, 2023] introduced ClipOGD, the first method achieving $\widetilde{O}(\sqrt{T})$ expected Neyman regret under mild conditions.
In this work, we propose adaptive designs with substantially stronger Neyman regret guarantees. In particular, we modify ClipOGD to obtain anytime $\widetilde{O}(\log T)$ Neyman regret under natural boundedness assumptions.
Further, in the setting where experimental units have pre-treatment covariates, we introduce and study a class of contextual ``multigroup'' Neyman regret guarantees: Given a set of possibly overlapping groups based on the covariates, the adaptive design outperforms each group's best non-adaptive designs. In particular, we develop a contextual adaptive design with $\widetilde{O}(\sqrt{T})$ anytime multigroup Neyman regret. We empirically validate the proposed designs through an array of experiments.
|
ATE, treatment effect, adaptive experimental design, efficient causal inference, Neyman, regret
|
Novel adaptive designs for efficient contextual/noncontextual finite-population estimation of ATE
| 13,414
|
2502.17427
|
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] |
https://github.com/amazon-science/adaptive-abtester
|
3,069
|
Probabilistic Factorial Experimental Design for Combinatorial Interventions
|
https://openreview.net/forum?id=RL6d53a5jj
|
[
"Divya Shyamal",
"Jiaqi Zhang",
"Caroline Uhler"
] |
Spotlight
|
A _combinatorial intervention_, consisting of multiple treatments applied to a single unit with potential interactive effects, has substantial applications in fields such as biomedicine, engineering, and beyond. Given $p$ possible treatments, conducting all possible $2^p$ combinatorial interventions can be laborious and quickly becomes infeasible as $p$ increases. Here we introduce the _probabilistic factorial experimental design_, formalized from how scientists perform lab experiments. In this framework, the experimenter selects a dosage for each possible treatment and applies it to a group of units. Each unit independently receives a random combination of treatments, sampled from a product Bernoulli distribution determined by the dosages. Additionally, the experimenter can carry out such experiments over multiple rounds, adapting the design in an active manner. We address the optimal experimental design problem within a novel intervention model that imposes bounded-degree interactions between treatments. In the passive setting, we provide a closed-form solution for the near-optimal design. Our results prove that a dosage of $\frac{1}{2}$ for each treatment is optimal up to a factor of $1+O(\frac{\ln(n)}{n})$ for estimating any $k$-way interaction model, regardless of $k$, and imply that $O\big(kp^{3k}\ln(p)\big)$ observations are required to accurately estimate this model. For the multi-round setting, we provide a near-optimal acquisition function that can be numerically optimized. We also explore several extensions of the design problem and finally validate our findings through simulations.
|
Experimental Design, Factorial Experiment, Combinatorial Interventions
| null | 13,345
|
2506.03363
|
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] | null |
3,070
|
From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models
|
https://openreview.net/forum?id=zdOGBRQEbz
|
[
"Etowah Adams",
"Liam Bai",
"Minji Lee",
"Yiyang Yu",
"Mohammed AlQuraishi"
] |
Spotlight
|
Protein language models (pLMs) are powerful predictors of protein structure and function, learning through unsupervised training on millions of protein sequences. pLMs are thought to capture common motifs in protein sequences, but the specifics of pLM features are not well understood. Identifying these features would not only shed light on how pLMs work, but potentially uncover novel protein biology––studying the model to study the biology. Motivated by this, we train sparse autoencoders (SAEs) on the residual stream of a pLM, ESM-2. By characterizing SAE features, we determine that pLMs use a combination of generic features and family-specific features to represent a protein. In addition, we demonstrate how known sequence determinants of properties such as thermostability and subcellular localization can be identified by linear probing of SAE features. For predictive features without known functional associations, we hypothesize their role in unknown mechanisms and provide visualization tools to aid their interpretation. Our study gives a better understanding of the limitations of pLMs, and demonstrates how SAE features can be used to help generate hypotheses for biological mechanisms. We release our code, model weights, and feature visualizer.
|
protein language model, sparse autoencoder, mechanistic interpretability, scientific discovery
|
We train and evaluate SAEs to identify interpretable features in pLMs and show their potential for scientific discovery.
| 13,328
| null |
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] |
https://github.com/etowahadams/interprot
|
3,071
|
CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation
|
https://openreview.net/forum?id=IYLNdCII48
|
[
"Aditya Gorla",
"Ryan Wang",
"Zhengtong Liu",
"Ulzee An",
"Sriram Sankararaman"
] |
Spotlight
|
We present CACTI, a masked autoencoding approach for imputing tabular data that leverages the structure in missingness patterns and contextual information. Our approach employs a novel median truncated copy masking training strategy that encourages the model to learn from empirical patterns of missingness while incorporating semantic relationships between features — captured by column names and text descriptions — to better represent feature dependence. These dual sources of inductive bias enable CACTIto outperform state-of-the-art methods — an average $R^2$ gain of 7.8\% over the next best method (13.4%, 6.1%, and 5.3% under missing not at random, at random and completely at random, respectively) — across a diverse range of datasets and missingness conditions. Our results highlight the value of leveraging dataset-specific contextual information and missingness patterns to enhance imputation performance.
|
Tabular data imputation, Inductive bias, Context awareness, Data-driven masking
|
A novel masked autoencoding imputation method that learns from real missingness patterns and feature context
| 13,310
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2506.02306
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] |
https://github.com/sriramlab/CACTI
|
3,072
|
PASS: Private Attributes Protection with Stochastic Data Substitution
|
https://openreview.net/forum?id=Yv416IYTFp
|
[
"Yizhuo Chen",
"Chun-Fu Chen",
"Hsiang Hsu",
"Shaohan Hu",
"Tarek F. Abdelzaher"
] |
Spotlight
|
The growing Machine Learning (ML) services require extensive collections of user data, which may inadvertently include people's private information irrelevant to the services. Various studies have been proposed to protect private attributes by removing them from the data while maintaining the utilities of the data for downstream tasks. Nevertheless, as we theoretically and empirically show in the paper, these methods reveal severe vulnerability because of a common weakness rooted in their adversarial training based strategies. To overcome this limitation, we propose a novel approach, PASS, designed to stochastically substitute the original sample with another one according to certain probabilities, which is trained with a novel loss function soundly derived from information-theoretic objective defined for utility-preserving private attributes protection. The comprehensive evaluation of PASS on various datasets of different modalities, including facial images, human activity sensory signals, and voice recording datasets, substantiates PASS's effectiveness and generalizability.
|
Privacy Protection, Utility Preservation, Information Theory
| null | 13,173
|
2506.07308
|
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] | null |
3,073
|
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
|
https://openreview.net/forum?id=13HPTmZKbM
|
[
"Sunny Sanyal",
"Hayden Prairie",
"Rudrajit Das",
"Ali Kavis",
"Sujay Sanghavi"
] |
Spotlight
|
Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as "catastrophic forgetting". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a *sample weighting scheme for the fine-tuning data* solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model. Our approach is orthogonal and yet complementary to existing methods; while such methods mostly operate on parameter or gradient space, we concentrate on the sample space. We theoretically analyze the impact of fine-tuning with our method in a linear setting, showing that it stalls learning in a certain subspace, which inhibits overfitting to the target task. We empirically demonstrate the efficacy of our method on both language and vision tasks. As an example, when fine-tuning Gemma 2 2B on MetaMathQA, our method results in only a $0.8$% drop in accuracy on GSM8K (another math dataset) compared to standard fine-tuning, while preserving $5.4$% more accuracy on the pre-training datasets.
|
Catastrophic Forgetting, Sample Weighting, Fine-tuning, Pre-trained
|
We propose a sample-weighting scheme to mitigate catastrophic forgetting during fine-tuning, demonstrating its effectiveness in vision and language tasks while also providing a theoretical analysis for linear models.
| 13,043
|
2502.02797
|
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] |
https://github.com/sanyalsunny111/FLOW_finetuning
|
3,074
|
Not All Wrong is Bad: Using Adversarial Examples for Unlearning
|
https://openreview.net/forum?id=BkrIQPREkn
|
[
"Ali Ebrahimpour-Boroojeny",
"Hari Sundaram",
"Varun Chandrasekaran"
] |
Spotlight
|
Machine unlearning, where users can request the deletion of a forget dataset, is becoming increasingly important because of numerous privacy regulations. Initial works on "exact'' unlearning (e.g., retraining) incur large computational overheads. However, while computationally inexpensive, "approximate'' methods have fallen short of reaching the effectiveness of exact unlearning: models produced fail to obtain comparable accuracy and prediction confidence on both the forget and test (i.e., unseen) dataset. Exploiting this observation, we propose a new unlearning method, Adversarial Machine UNlearning (AMUN), that outperforms prior state-of-the-art (SOTA) methods for image classification. AMUN lowers the confidence of the model on the forget samples by fine-tuning the model on their corresponding adversarial examples. Adversarial examples naturally belong to the distribution imposed by the model on the input space; fine-tuning the model on the adversarial examples closest to the corresponding forget samples (a) localizes the changes to the decision boundary of the model around each forget sample and (b) avoids drastic changes to the global behavior of the model, thereby preserving the model's accuracy on test samples. Using AMUN for unlearning a random 10% of CIFAR-10 samples, we observe that even SOTA membership inference attacks cannot do better than random guessing.
|
Machine Unlearning, Adversarial example, Fine-tuning
| null | 13,000
| null |
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0.02931886725127697,
0.007141484413295984,
-0.03101353533565998,
0.02020188234746456
] |
https://github.com/Ali-E/AMUN
|
3,075
|
Training Deep Learning Models with Norm-Constrained LMOs
|
https://openreview.net/forum?id=2Oqm2IzTy9
|
[
"Thomas Pethick",
"Wanyun Xie",
"Kimon Antonakopoulos",
"Zhenyu Zhu",
"Antonio Silveti-Falls",
"Volkan Cevher"
] |
Spotlight
|
In this work, we study optimization methods that leverage the linear minimization oracle (LMO) over a norm-ball. We propose a new stochastic family of algorithms that uses the LMO to adapt to the geometry of the problem and, perhaps surprisingly, show that they can be applied to unconstrained problems. The resulting update rule unifies several existing optimization methods under a single framework. Furthermore, we propose an explicit choice of norm for deep architectures, which, as a side benefit, leads to the transferability of hyperparameters across model sizes. Experimentally, we demonstrate significant speedups on nanoGPT training without any reliance on Adam. The proposed method is memory-efficient, requiring only one set of model weights and one set of gradients, which can be stored in half-precision.
|
non-euclidean, linear minimization oracle, deep learning, spectral norm
| null | 12,949
|
2502.07529
|
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] |
https://github.com/LIONS-EPFL/scion
|
3,076
|
From Language Models over Tokens to Language Models over Characters
|
https://openreview.net/forum?id=sQS0roNQZR
|
[
"Tim Vieira",
"Benjamin LeBrun",
"Mario Giulianelli",
"Juan Luis Gastaldi",
"Brian DuSell",
"John Terilla",
"Timothy J. O'Donnell",
"Ryan Cotterell"
] |
Spotlight
|
Modern language models are internally—and mathematically—distributions over *token* strings rather than *character* strings, posing numerous challenges for programmers building user applications on top of them. For example, if a prompt is specified as a character string, it must be tokenized before passing it to the token-level language model. Thus, the tokenizer and consequent processing are very sensitive to the specification of the prompt (e.g., whether the prompt ends with a space or not). This paper presents algorithms for converting token-level language models to character-level ones. We present both exact and approximate algorithms. In the empirical portion of the paper, we benchmark the practical runtime and approximation quality. Across four publicly available language models, we find that—even with a small computation budget—our method is able to accurately approximate the character-level distribution at reasonably fast speeds, and that a significant improvement in the language model's compression rate (bits/byte) is achieved.
|
tokenization, character, bytes, tokens, language models, probabilistic reasoning, probabilistic inference
|
Efficient algorithms for character-level conditioning of LLMs
| 12,880
|
2412.03719
|
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] |
https://github.com/genlm/genlm-bytes
|
3,077
|
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety
|
https://openreview.net/forum?id=GFsMJKt9Kp
|
[
"Zihan Guan",
"Mengxuan Hu",
"Ronghang Zhu",
"Sheng Li",
"Anil Vullikanti"
] |
Spotlight
|
Recent studies have uncovered a troubling vulnerability in the fine-tuning stage of large language models (LLMs): even fine-tuning on entirely benign datasets can lead to a significant increase in the harmfulness of LLM outputs. Building on this finding, our red teaming study takes this threat one step further by developing a more effective attack. Specifically, we analyze and identify samples within benign datasets that contribute most to safety degradation, then fine-tune LLMs exclusively on these samples. We approach this problem from an outlier detection perspective and propose Self-Inf-N, to detect and extract outliers for fine-tuning. Our findings reveal that fine-tuning LLMs on 100 outlier samples selected by Self-Inf-N in the benign datasets severely compromises LLM safety alignment. Extensive experiments across seven mainstream LLMs demonstrate that our attack exhibits high transferability across different architectures and remains effective in practical scenarios. Alarmingly, our results indicate that most existing mitigation strategies fail to defend against this attack, underscoring the urgent need for more robust alignment safeguards. Codes are available at https://github.com/GuanZihan/Benign-Samples-Matter.
|
Fine-tuning, Safety Alignment, Data Selection
| null | 12,855
|
2505.06843
|
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] |
https://github.com/GuanZihan/Benign-Samples-Matter
|
3,078
|
Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation
|
https://openreview.net/forum?id=DoDXFkF10S
|
[
"Alessandro Palma",
"Sergei Rybakov",
"Leon Hetzel",
"Stephan Günnemann",
"Fabian J Theis"
] |
Spotlight
|
Latent space interpolations are a powerful tool for navigating deep generative models in applied settings. An example is single-cell RNA sequencing, where existing methods model cellular state transitions as latent space interpolations with variational autoencoders, often assuming linear shifts and Euclidean geometry. However, unless explicitly enforced, linear interpolations in the latent space may not correspond to geodesic paths on the data manifold, limiting methods that assume Euclidean geometry in the data representations. We introduce FlatVI, a novel training framework that regularises the latent manifold of discrete-likelihood variational autoencoders towards Euclidean geometry, specifically tailored for modelling single-cell count data. By encouraging straight lines in the latent space to approximate geodesic interpolations on the decoded single-cell manifold, FlatVI enhances compatibility with downstream approaches that assume Euclidean latent geometry. Experiments on synthetic data support the theoretical soundness of our approach, while applications to time-resolved single-cell RNA sequencing data demonstrate improved trajectory reconstruction and manifold interpolation.
|
scRNA-seq, Riemannian geometry, representation learning, trajectory inference, VAEs, statistical manifolds
|
We enforce a local Euclidean geometry in the latent manifold of scRNA-seq VAEs to improve downstream applications.
| 12,560
| null |
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] | null |
3,079
|
RE-Bench: Evaluating Frontier AI R&D Capabilities of Language Model Agents against Human Experts
|
https://openreview.net/forum?id=3rB0bVU6z6
|
[
"Hjalmar Wijk",
"Tao Roa Lin",
"Joel Becker",
"Sami Jawhar",
"Neev Parikh",
"Thomas Broadley",
"Lawrence Chan",
"Michael Chen",
"Joshua M Clymer",
"Jai Dhyani",
"Elena Ericheva",
"Katharyn Garcia",
"Brian Goodrich",
"Nikola Jurkovic",
"Megan Kinniment",
"Aron Lajko",
"Seraphina Nix",
"Lucas Jun Koba Sato",
"William Saunders",
"Maksym Taran",
"et al. (2 additional authors not shown)"
] |
Spotlight
|
Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations for AI R&D capabilities, and none that are highly realistic and have a direct comparison to human performance. We introduce RE-Bench (Research Engineering Benchmark, V1), which consists of 7 challenging, open-ended ML research engineering environments and data from 71 8-hour attempts by 61 distinct human experts. We confirm that our experts make progress in the environments given 8 hours, with 82% of expert attempts achieving a non-zero score and 24% matching or exceeding our strong reference solutions. We compare humans to several public frontier models through best-of-$k$ with varying time budgets and agent designs, and find that the best AI agents achieve a score 4× higher than human experts when both are given a total time budget of 2 hours per environment. However, humans currently display better returns to increasing time budgets, narrowly exceeding the top AI agent scores given an 8-hour budget, and achieving 2× the score of the top AI agent when both are given 32 total hours (across different attempts).
|
Machine Learning, LLM evaluations, Agents, AI R&D, Benchmarks
|
We introduce RE-Bench, an AI R&D benchmark for AI agents, with 7 challenging, open-ended ML research engineering environments and comparisons against expert human baselines.
| 12,503
| null |
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] |
https://github.com/METR/RE-Bench
|
3,080
|
PokéChamp: an Expert-level Minimax Language Agent
|
https://openreview.net/forum?id=SnZ7SKykHh
|
[
"Seth Karten",
"Andy Luu Nguyen",
"Chi Jin"
] |
Spotlight
|
We introduce PokéChamp, a minimax agent powered by Large Language Models (LLMs) for Pokémon battles. Built on a general framework for two-player competitive games, PokéChamp leverages the generalist capabilities of LLMs to enhance minimax tree search. Specifically, LLMs replace three key modules: (1) player action sampling, (2) opponent modeling, and (3) value function estimation, enabling the agent to effectively utilize gameplay history and human knowledge to reduce the search space and address partial observability. Notably, our framework requires no additional LLM training. We evaluate PokéChamp in the popular Gen 9 OU format. When powered by GPT-4o, it achieves a win rate of 76\% against the best existing LLM-based bot and 84\% against the strongest rule-based bot, demonstrating its superior performance. Even with an open-source 8-billion-parameter Llama 3.1 model, PokéChamp consistently outperforms the previous best LLM-based bot, Pokéllmon powered by GPT-4o, with a 64\% win rate. PokéChamp attains a projected Elo of 1300-1500 on the Pokémon Showdown online ladder, placing it among the top 30\%-10\% of human players. In addition, this work compiles the largest real-player Pokémon battle dataset, featuring over 3 million games, including more than 500k high-Elo matches. Based on this dataset, we establish a series of battle benchmarks and puzzles to evaluate specific battling skills. We further provide key updates to the local game engine. This work establishes Pokémon as a benchmark to integrate LLM technologies with game-theoretic algorithms addressing general multi-agent problems. Videos, code, and dataset are available online.
|
multi-agent systems, LLM Agents, competitive games, partially observable, test-time compute, pokemon
|
PokéChamp, an LLM-powered AI agent for Pokémon battles, outperforms existing bots and achieves expert-level performance against human players using minimax search with LLM-based action sampling, opponent modeling, and value calculation.
| 12,489
| null |
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] |
https://github.com/sethkarten/pokechamp
|
3,081
|
G-Adaptivity: optimised graph-based mesh relocation for finite element methods
|
https://openreview.net/forum?id=pyIXyl4qFx
|
[
"James Rowbottom",
"Georg Maierhofer",
"Teo Deveney",
"Eike Hermann Müller",
"Alberto Paganini",
"Katharina Schratz",
"Pietro Lio",
"Carola-Bibiane Schönlieb",
"Chris Budd"
] |
Spotlight
|
We present a novel, and effective, approach to achieve optimal mesh relocation in finite element methods (FEMs). The cost and accuracy of FEMs is critically dependent on the choice of mesh points. Mesh relocation (r-adaptivity) seeks to optimise the mesh geometry to obtain the best solution accuracy at given computational budget. Classical r-adaptivity relies on the solution of a separate nonlinear ``meshing'' PDE to determine mesh point locations. This incurs significant cost at remeshing, and relies on estimates that relate interpolation- and FEM-error. Recent machine learning approaches have focused on the construction of fast surrogates for such classical methods. Instead, our new approach trains a graph neural network (GNN) to determine mesh point locations by directly minimising the FE solution error from the PDE system Firedrake to achieve higher solution accuracy. Our GNN architecture closely aligns the mesh solution space to that of classical meshing methodologies, thus replacing classical estimates for optimality with a learnable strategy. This allows for rapid and robust training and results in an extremely efficient and effective GNN approach to online r-adaptivity. Our method outperforms both classical, and prior ML, approaches to r-adaptive meshing. In particular, it achieves lower FE solution error, whilst retaining the significant speed-up over classical methods observed in prior ML work.
|
Mesh Adaptation, r-Adaptivity, Monge–Ampere, Deep Learning, Finite Element Methods, Partial Differential Equations, Neural Network, Graph Neural Networks
|
A graph neural network approach to mesh relocation in finite element methods that directly minimises solution error, achieving higher accuracy compared to classical and prior ML-based r-adaptivity techniques.
| 12,483
| null |
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] |
https://github.com/JRowbottomGit/g-adaptivity
|
3,082
|
Mastering Board Games by External and Internal Planning with Language Models
|
https://openreview.net/forum?id=KKwBo3u3IW
|
[
"John Schultz",
"Jakub Adamek",
"Matej Jusup",
"Marc Lanctot",
"Michael Kaisers",
"Sarah Perrin",
"Daniel Hennes",
"Jeremy Shar",
"Cannada A. Lewis",
"Anian Ruoss",
"Tom Zahavy",
"Petar Veličković",
"Laurel Prince",
"Satinder Singh",
"Eric Malmi",
"Nenad Tomasev"
] |
Spotlight
|
Advancing planning and reasoning capabilities of Large Language Models (LLMs) is one of the key prerequisites towards unlocking their potential for performing reliably in complex and impactful domains. In this paper, we aim to demonstrate this across board games (Chess, Fischer Random / Chess960, Connect Four, and Hex), and we show that search-based planning can yield significant improvements in LLM game-playing strength. We introduce, compare and contrast two major approaches: In *external search*, the model guides Monte Carlo Tree Search (MCTS) rollouts and evaluations without calls to an external game engine, and in *internal search*, the model is trained to generate in-context a linearized tree of search and a resulting final choice. Both build on a language model pre-trained on relevant domain knowledge, reliably capturing the transition and value functions in the respective environments, with minimal hallucinations. We evaluate our LLM search implementations against game-specific state-of-the-art engines, showcasing substantial improvements in strength over the base model, and reaching Grandmaster-level performance in chess while operating closer to the human search budget. Our proposed approach, combining search with domain knowledge, is not specific to board games, hinting at more general future applications.
|
search, planning, language models, games, chess
|
We pre-trained an LLM capable of playing board games at a high level. We further introduce external and internal planning methods that achieve Grandmaster-level performance in chess while operating closer to the human search budget.
| 12,369
|
2412.12119
|
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] | null |
3,083
|
When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network
|
https://openreview.net/forum?id=oYyaVSqEFu
|
[
"Dong Xiao",
"Guangyao Chen",
"Peixi Peng",
"Yangru Huang",
"Yifan Zhao",
"Yongxing Dai",
"Yonghong Tian"
] |
Spotlight
|
Anomaly detection is essential for the safety and reliability of autonomous driving systems. Current methods often focus on detection accuracy but neglect response time, which is critical in time-sensitive driving scenarios. In this paper, we introduce real-time anomaly detection for autonomous driving, prioritizing both minimal response time and high accuracy. We propose a novel multimodal asynchronous hybrid network that combines event streams from event cameras with image data from RGB cameras. Our network utilizes the high temporal resolution of event cameras through an asynchronous Graph Neural Network and integrates it with spatial features extracted by a CNN from RGB images. This combination effectively captures both the temporal dynamics and spatial details of the driving environment, enabling swift and precise anomaly detection. Extensive experiments on benchmark datasets show that our approach outperforms existing methods in both accuracy and response time, achieving millisecond-level real-time performance.
|
Anomaly Detection, Multimodal Network, Autonomous Driving
|
We propose an asynchronous hybrid anomaly detection network that leverages event data to accurately detect anomalies during driving with extremely low response time.
| 12,326
|
2506.17457
|
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] |
https://github.com/PKU-XD/EventAD
|
3,084
|
Towards Better-than-2 Approximation for Constrained Correlation Clustering
|
https://openreview.net/forum?id=MkCnPNOLMk
|
[
"Andreas Kalavas",
"Evangelos Kipouridis",
"Nithin Varma"
] |
Spotlight
|
In the Correlation Clustering problem, we are given an undirected graph and are tasked with computing a clustering (partition of the nodes) that minimizes the sum of the number of edges across different clusters and the number of non-edges within clusters. In the constrained version of this problem, the goal is to compute a clustering that satisfies additional hard constraints mandating certain pairs to be in the same cluster and certain pairs to be in different clusters. Constrained Correlation Clustering is APX-Hard, and the best known approximation factor is 3 (van Zuylen et al. [SODA '07]). In this work, we show that in order to obtain a better-than-2 approximation, solving the (exponentially large) Constrained Cluster LP would be sufficient.
[The peer-reviewed version of this article claimed an efficient algorithm for solving the Constrained Cluster LP. An error in the proof, that the authors discovered after the review process, led them to revise the results to be conditional on the existence of a valid LP solution.]
|
Clustering, Constrained Correlation Clustering, Approximation
|
In this work, we show that in order to obtain a better-than-2 approximation for Constrained Correlation Clustering, solving the (exponentially large) Constrained Cluster LP would be sufficient.
| 12,312
| null |
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] | null |
3,085
|
AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
|
https://openreview.net/forum?id=K2CckZjNy0
|
[
"Zhengxuan Wu",
"Aryaman Arora",
"Atticus Geiger",
"Zheng Wang",
"Jing Huang",
"Dan Jurafsky",
"Christopher D Manning",
"Christopher Potts"
] |
Spotlight
|
Fine-grained steering of language model outputs is essential for safety and reliability. Prompting and finetuning are widely used to achieve these goals, but interpretability researchers have proposed a variety of representation-based techniques as well, including sparse autoencoders (SAEs), linear artificial tomography, supervised steering vectors, linear probes, and representation finetuning. At present, there is no benchmark for making direct comparisons between these proposals. Therefore, we introduce AxBench, a large-scale benchmark for steering and concept detection, and report experiments on Gemma-2-2B and 9B. For steering, we find that prompting outperforms all existing methods, followed by finetuning. For concept detection, representation-based methods such as difference-in-means, perform the best. On both evaluations, SAEs are not competitive. We introduce a novel weakly-supervised representational method (Rank-1 Representation Finetuning; ReFT-r1), which is competitive on both tasks while providing the interpretability advantages that prompting lacks. Along with AxBench, we train and publicly release SAE-scale feature dictionaries for ReFT-r1 and DiffMean.
|
LLMs, Interpretability, Sparse Autoencoders, Dictionary Learning
|
In this work, we introduce AxBench, a benchmark for evaluating LM control methods at scale using synthetic data.
| 12,232
|
2501.17148
|
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] |
https://github.com/stanfordnlp/axbench
|
3,086
|
Scaling Test-Time Compute Without Verification or RL is Suboptimal
|
https://openreview.net/forum?id=beeNgQEfe2
|
[
"Amrith Setlur",
"Nived Rajaraman",
"Sergey Levine",
"Aviral Kumar"
] |
Spotlight
|
Despite substantial advances in scaling test-time compute, an ongoing debate in the community is how it should be scaled up to enable continued and efficient improvements with scaling. There are largely two approaches: (i) distilling successful search or thinking traces; and (ii), using verification (e.g., 0/1 outcome rewards, or verifiers) to guide reinforcement learning (RL) and search algorithms. In this paper, we prove that finetuning LLMs with verifier-based (VB) methods based on RL or search is far superior to verifier-free (VF) approaches based on distilling or cloning search traces, given a fixed amount of compute/data budget. Further, we show that as we scale test-time compute (measured as the output token length) and training data, suboptimality of VF methods scales poorly compared to VB when the base pre-trained LLM presents a heterogeneous distribution over correct solution traces (e.g., different lengths, styles, etc.) and admits a non-sharp distribution over rewards on traces sampled from it. We formalize this condition using anti-concentration [Erdős 1945], implying a stronger result that VB methods scale better asymptotically, with the performance gap between VB and VF widening as test-time budget grows.
We corroborate our theory empirically on didactic and math reasoning problems with 3/8/32B-sized pre-trained LLMs, where we find verification is crucial for scaling test-time compute.
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LLMs, test-time compute, verification, RL, finetuning, reasoning, RL theory
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We show that using verification signal scales test-time compute better than verification free methods, asymptotically.
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2502.12118
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] | null |
3,087
|
LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression
|
https://openreview.net/forum?id=9u5hPIcr6j
|
[
"Haotian Wu",
"Gongpu Chen",
"Pier Luigi Dragotti",
"Deniz Gunduz"
] |
Spotlight
|
We introduce and validate the lottery codec hypothesis, which states that untrained subnetworks within randomly initialized networks can serve as synthesis networks for overfitted image compression, achieving rate-distortion (RD) performance comparable to trained networks. This hypothesis leads to a new paradigm for image compression by encoding image statistics into the network substructure. Building on this hypothesis, we propose LotteryCodec, which overfits a binary mask to an individual image, leveraging an over-parameterized and randomly initialized network shared by the encoder and the decoder. To address over-parameterization challenges and streamline subnetwork search, we develop a rewind modulation mechanism that improves the RD performance. LotteryCodec outperforms VTM and sets a new state-of-the-art in single-image compression. LotteryCodec also enables adaptive decoding complexity through adjustable mask ratios, offering flexible compression solutions for diverse device constraints and application requirements.
|
Implicit neural representation, source coding, overfitted image compression, lottery codec hypothesis, low-complexity image codec.
| null | 12,171
|
2507.01204
|
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] | null |
3,088
|
Efficiently Vectorized MCMC on Modern Accelerators
|
https://openreview.net/forum?id=Mlmpf4Izrj
|
[
"Hugh Dance",
"Pierre Glaser",
"Peter Orbanz",
"Ryan P Adams"
] |
Spotlight
|
With the advent of automatic vectorization tools (e.g., JAX's vmap), writing multi-chain MCMC algorithms is often now as simple as invoking those tools on single-chain code. Whilst convenient, for various MCMC algorithms this results in a synchronization problem---loosely speaking, at each iteration all chains running in parallel must wait until the last chain has finished drawing its sample. In this work, we show how to design single-chain MCMC algorithms in a way that avoids synchronization overheads when vectorizing with tools like vmap, by using the framework of finite state machines (FSMs). Using a simplified model, we derive an exact theoretical form of the obtainable speed-ups using our approach, and use it to make principled recommendations for optimal algorithm design. We implement several popular MCMC algorithms as FSMs, including Elliptical Slice Sampling, HMC-NUTS, and Delayed Rejection, demonstrating speed-ups of up to an order of magnitude in experiments.
|
Markov Chain Monte Carlo, MCMC, Parallel Computing
| null | 12,121
|
2503.17405
|
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] |
https://github.com/hwdance/jax-fsm-mcmc
|
3,089
|
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards
|
https://openreview.net/forum?id=5IpVe9PH14
|
[
"Chenlu Ye",
"Yujia Jin",
"Alekh Agarwal",
"Tong Zhang"
] |
Spotlight
|
Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range $[0, R]$, and their regret scales polynomially with this reward range $R$. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general function approximation. When the variance of the reward at each round is known, we use a variance-weighted regression approach and establish a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range $R$ as well as the number of rounds $T$. For the unknown-variance case, we further propose a careful peeling-based algorithm and remove the need for cumbersome variance estimation. With additional dependence on the fourth moment, our algorithm also enjoys a variance-based bound with logarithmic reward-range dependence. Moreover, we demonstrate the optimality of the leading-order term in our regret bound through a matching lower bound.
|
Heavy-tailed Rewards, Contextual bandits, Genenral function approximation
| null | 12,097
|
2502.02486
|
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] | null |
3,090
|
On the Power of Context-Enhanced Learning in LLMs
|
https://openreview.net/forum?id=Gn6L4QRKf7
|
[
"Xingyu Zhu",
"Abhishek Panigrahi",
"Sanjeev Arora"
] |
Spotlight
|
We formalize a new concept for LLMs, **context-enhanced learning**. It involves standard gradient-based learning on text except that the context is enhanced with additional data on which no auto-regressive gradients are computed. This setting is a gradient-based analog of usual in-context learning (ICL) and appears in some recent works.
Using a multi-step reasoning task, we prove in a simplified setting that context-enhanced learning can be **exponentially more sample-efficient** than standard learning when the model is capable of ICL. At a mechanistic level, we find that the benefit of context-enhancement arises from a more accurate gradient learning signal.
We also experimentally demonstrate that it appears hard to detect or recover learning materials that were used in the context during training. This may have implications for data security as well as copyright.
|
In-Context Learning, Sample Efficiency, Knowledge Internalization, Privileged Information, Multi-step reasoning
|
We formalize Context-Enhanced Learning, a paradigm where LLMs leverage context without direct gradient updates, achieving exponential sample efficiency gains over standard autoregressive training when the model has ICL capabilities
| 12,095
|
2503.01821
|
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] |
https://github.com/princeton-pli/Context-Enhanced-Learning
|
3,091
|
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
|
https://openreview.net/forum?id=bkauyuzBN4
|
[
"Jan Schuchardt",
"Mina Dalirrooyfard",
"Jed Guzelkabaagac",
"Anderson Schneider",
"Yuriy Nevmyvaka",
"Stephan Günnemann"
] |
Spotlight
|
Many forms of sensitive data, such as web traffic, mobility data, or hospital occupancy, are inherently sequential. The standard method for training machine learning models while ensuring privacy for units of sensitive information, such as individual hospital visits, is differentially private stochastic gradient descent (DP-SGD). However, we observe in this work that the formal guarantees of DP-SGD are incompatible with time series specific tasks like forecasting, since they rely on the *privacy amplification* attained by training on small, unstructured batches sampled from an unstructured dataset. In contrast, batches for forecasting are generated by (1) sampling sequentially structured time series from a dataset, (2) sampling contiguous subsequences from these series, and (3) partitioning them into context and ground-truth forecast windows. We theoretically analyze the privacy amplification attained by this *structured subsampling* to enable the training of forecasting models with sound and tight event- and user-level privacy guarantees. Towards more private models, we additionally prove how data augmentation amplifies privacy in self-supervised training of sequence models. Our empirical evaluation demonstrates that amplification by structured subsampling enables the training of forecasting models with strong formal privacy guarantees.
|
Differential Privacy, Privacy Amplification, Privacy Accounting, Time Series, Forecasting
|
We analyze the privacy of DP-SGD adapted to time series forecasting in a domain- and task-specific manner.
| 11,936
|
2502.02410
|
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] | null |
3,092
|
Functional Alignment Can Mislead: Examining Model Stitching
|
https://openreview.net/forum?id=glLqTK9En3
|
[
"Damian Smith",
"Harvey Mannering",
"Antonia Marcu"
] |
Spotlight
|
A common belief in the representational comparison literature is that if two representations can be functionally aligned, they must capture similar information. In this paper we focus on model stitching and show that models can be functionally aligned, but represent very different information. Firstly, we show that discriminative models with very different biases can be stitched together. We then show that models trained to solve entirely different tasks on different data modalities, and even clustered random noise, can be successfully stitched into MNIST or ImageNet-trained models. We end with a discussion of the wider impact of our results on the community's current beliefs. Overall, our paper draws attention to the need to correctly interpret the results of such functional similarity measures and highlights the need for approaches that capture informational similarity.
|
stitching, model comparison, representation comparison, representation alignment, functional alignment, functional similarity
|
Informational similarity must be accounted for when comparing models; model stitching does not do this.
| 11,927
| null |
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] |
https://github.com/DHLSmith/stitching
|
3,093
|
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
|
https://openreview.net/forum?id=4qIP1sXcR1
|
[
"Utkarsh Saxena",
"Sayeh Sharify",
"Kaushik Roy",
"Xin Wang"
] |
Spotlight
|
Post-training quantization (PTQ) of large language models (LLMs) holds the promise in reducing the prohibitive computational cost at inference time. Quantization of all weight, activation and key-value (KV) cache tensors to 4-bit without significantly degrading generalizability is challenging, due to the high quantization error caused by extreme outliers in activations. To tackle this problem, we propose ResQ, a PTQ method that pushes further the state-of-the-art. By means of principal component analysis (PCA), it identifies a low-rank subspace (in practice 1/8 of the hidden dimension) in which activation variances are highest, and keep the coefficients within this subspace in high precision, e.g.~8-bit, while quantizing the rest to 4-bit. Within each subspace, invariant random rotation is applied to further suppress outliers. We show that this is a provably optimal mixed precision quantization scheme that minimizes error. With the Llama and Qwen2.5 families of models, we demonstrate that ResQ outperforms recent uniform and mixed precision PTQ methods on a variety of benchmarks, achieving up to 33\% lower perplexity on Wikitext than the next best method SpinQuant, and upto 3X speedup over 16-bit baseline. Anonymous code repository available at https://anonymous.4open.science/r/project-resq-2142.
|
quantization, large language models, low precision computation, kv cache compression, efficient language models
|
We perform weight/activation/kv cache quantization of large language models to 4-bit while keeping only 1/8 channels in 8-bit.
| 11,902
|
2412.14363
|
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] |
https://github.com/utkarsh-dmx/project-resq
|
3,094
|
GMAIL: Generative Modality Alignment for generated Image Learning
|
https://openreview.net/forum?id=u6xeKVHS6K
|
[
"Shentong Mo",
"Sukmin Yun"
] |
Spotlight
|
Generative models have made it possible to synthesize highly realistic images, potentially providing an abundant data source for training machine learning models. Despite the advantages of these synthesizable data sources, the indiscriminate use of generated images as real images for training can even cause mode collapse due to modality discrepancies between real and synthetic domains. In this paper, we propose a novel framework for discriminative use of generated images, coined \textit{GMAIL}, that explicitly treats generated images as a separate modality from real images. Instead of indiscriminately replacing real images with generated ones in the pixel space, our approach bridges the two distinct modalities in the same latent space through a multi-modal learning approach. To be specific, we first fine-tune a model exclusively on generated images using a cross-modality alignment loss and then employ this aligned model to further train various vision-language models with generated images. By aligning the two modalities, our approach effectively leverages the benefits of recent advances in generative models, thereby boosting the effectiveness of generated image learning across a range of vision-language tasks. Our framework can be easily incorporated with various vision-language models, and we demonstrate its efficacy throughout extensive experiments. For example, our framework significantly improves performance on image captioning, zero-shot image retrieval, zero-shot image classification, and long caption retrieval tasks. It also shows positive generated data scaling trends and notable enhancements in the captioning performance of the large multimodal model, LLaVA.
|
diffusion models, generated visual learning, vision-language models
|
In this paper, we propose a novel framework for discriminative use of generated images that explicitly treats generated images as a separate modality from real images.
| 11,875
| null |
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] |
https://github.com/GMAIL-IMG/GMAIL
|
3,095
|
Flopping for FLOPs: Leveraging Equivariance for Computational Efficiency
|
https://openreview.net/forum?id=DzLP43CbiX
|
[
"Georg Bökman",
"David Nordström",
"Fredrik Kahl"
] |
Spotlight
|
Incorporating geometric invariance into neural networks enhances parameter efficiency but typically increases computational costs.
This paper introduces new equivariant neural networks
that preserve symmetry while maintaining a comparable number of floating-point operations (FLOPs) per parameter to standard non-equivariant networks.
We focus on horizontal mirroring (flopping) invariance, common in many computer vision tasks.
The main idea is to parametrize the feature spaces in terms of mirror-symmetric and mirror-antisymmetric features, i.e., irreps of the flopping group.
This decomposes the linear layers to be block-diagonal, requiring half the number of FLOPs.
Our approach reduces both FLOPs and wall-clock time,
providing a practical solution for efficient, scalable symmetry-aware architectures.
|
Equivariance, efficiency, scalable, image classification, vision
|
Equivariant neural networks for image input are scalable.
| 11,798
|
2502.05169
|
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] |
https://github.com/georg-bn/flopping-for-flops
|
3,096
|
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective
|
https://openreview.net/forum?id=VpBBw1bL47
|
[
"yuanhong zhang",
"Muyao Yuan",
"Weizhan Zhang",
"Tieliang Gong",
"Wen Wen",
"Jiangyong Ying",
"Weijie Shi"
] |
Spotlight
|
The Segment Anything Model (SAM), a vision foundation model, exhibits impressive zero-shot capabilities in general tasks but struggles in specialized domains. Parameter-efficient fine-tuning (PEFT) is a promising approach to unleash the potential of SAM in novel scenarios. However, existing PEFT methods for SAM neglect the domain-invariant relations encoded in the pre-trained model. To bridge this gap, we propose InfoSAM, an information-theoretic approach that enhances SAM fine-tuning by distilling and preserving its pre-trained segmentation knowledge. Specifically, we formulate the knowledge transfer process as two novel mutual information-based objectives: (i) to compress the domain-invariant relation extracted from pre-trained SAM, excluding pseudo-invariant information as possible, and (ii) to maximize mutual information between the relational knowledge learned by the teacher (pre-trained SAM) and the student (fine-tuned model). The proposed InfoSAM establishes a robust distillation framework for PEFT of SAM. Extensive experiments across diverse benchmarks validate InfoSAM's effectiveness in improving SAM family's performance on real-world tasks, demonstrating its adaptability and superiority in handling specialized scenarios. The code and models are available at https://muyaoyuan.github.io/InfoSAM_Page.
|
Segment Anything Model, Parameter-efficient fine-tuning, Mutual information, Knowledge distillation
|
InfoSAM improves SAM's performance on specialized tasks by using an information-theoretic approach to distill and preserve domain-invariant knowledge during fine-tuning.
| 11,749
|
2505.21920
|
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] | null |
3,097
|
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
|
https://openreview.net/forum?id=r9HlTuCQfr
|
[
"Charita Dellaporta",
"Patrick O'Hara",
"Theodoros Damoulas"
] |
Spotlight
|
Decision making under uncertainty is challenging as the data-generating process (DGP) is often unknown. Bayesian inference proceeds by estimating the DGP through posterior beliefs on the model’s parameters. However, minimising the expected risk under these beliefs can lead to suboptimal decisions due to model uncertainty or limited, noisy observations. To address this, we introduce Distributionally Robust Optimisation with Bayesian Ambiguity Sets (DRO-BAS) which hedges against model uncertainty by optimising the worst-case risk over a posterior-informed ambiguity set. We provide two such sets, based on the posterior expectation (DRO-BAS(PE)) or the posterior predictive (DRO-BAS(PP)) and prove that both admit, under conditions, strong dual formulations leading to efficient single-stage stochastic programs which are solved with a sample average approximation. For DRO-BAS(PE), this covers all conjugate exponential family members while for DRO-BAS(PP) this is shown under conditions on the predictive's moment generating function. Our DRO-BAS formulations outperform existing Bayesian DRO on the Newsvendor problem and achieve faster solve times with comparable robustness on the Portfolio problem.
|
Distributional Robustness, Bayesian Inference, Stochastic Optimisation
| null | 11,717
|
2411.16829
|
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] |
https://github.com/PatrickOHara/mis-dro-code
|
3,098
|
Hyperspherical Normalization for Scalable Deep Reinforcement Learning
|
https://openreview.net/forum?id=kfYxyvCYQ4
|
[
"Hojoon Lee",
"Youngdo Lee",
"Takuma Seno",
"Donghu Kim",
"Peter Stone",
"Jaegul Choo"
] |
Spotlight
|
Scaling up the model size and computation has brought consistent performance improvements in supervised learning.
However, this lesson often fails to apply to reinforcement learning (RL) because training the model on non-stationary data easily leads to overfitting and unstable optimization.
In response, we introduce SimbaV2, a novel RL architecture designed to stabilize optimization by (i) constraining the growth of weight and feature norm by hyperspherical normalization; and (ii) using a distributional value estimation with reward scaling to maintain stable gradients under varying reward magnitudes. Using the soft actor-critic as a base algorithm, SimbaV2 scales up effectively with larger models and greater compute, achieving state-of-the-art performance on 57 continuous control tasks across 4 domains.
|
reinforcement learning, normalization
|
Normalizing weights and features on unit norm hypersphere allows scaling up parameters and computations in RL
| 11,664
|
2502.15280
|
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] |
https://github.com/dojeon-ai/SimbaV2
|
3,099
|
Score-of-Mixture Training: One-Step Generative Model Training Made Simple via Score Estimation of Mixture Distributions
|
https://openreview.net/forum?id=zk5k2NQcEA
|
[
"Tejas Jayashankar",
"Jongha Jon Ryu",
"Gregory W. Wornell"
] |
Spotlight
|
We propose *Score-of-Mixture Training* (SMT), a novel framework for training one-step generative models by minimizing a class of divergences called the
$\alpha$-skew Jensen–Shannon divergence. At its core, SMT estimates the score of mixture distributions between real and fake samples across multiple noise levels.
Similar to consistency models, our approach supports both training from scratch (SMT) and distillation using a pretrained diffusion model, which we call *Score-of-Mixture Distillation* (SMD).
It is simple to implement, requires minimal hyperparameter tuning, and ensures stable training. Experiments on CIFAR-10 and ImageNet 64×64 show that SMT/SMD are competitive with and can even outperform existing methods.
|
one-step generation, skew Jensen-Shannon Divergence, diffusion models, score estimation
|
We introduce Score-of-Mixture Training, a simple and stable framework for training one-step generative models from scratch using the α-skew Jensen–Shannon divergence by estimating the scores of mixture distribution across multiple noise levels.
| 11,626
|
2502.09609
|
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] |
https://github.com/tkj516/score-of-mixture-training
|
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