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2,700
|
Joker: Joint Optimization Framework for Lightweight Kernel Machines
|
https://openreview.net/forum?id=hRWcT3AH7Y
|
[
"Junhong Zhang",
"Zhihui Lai"
] |
Poster
|
Kernel methods are powerful tools for nonlinear learning with well-established theory.
The scalability issue has been their long-standing challenge.
Despite the existing success,
there are two limitations in large-scale kernel methods:
(i) The memory overhead is too high for users to afford;
(ii) existing efforts mainly focus on kernel ridge regression (KRR), while other models lack study.
In this paper, we propose **Joker**, a joint optimization framework for diverse kernel models, including KRR, logistic regression, and support vector machines.
We design a dual block coordinate descent method with trust region (DBCD-TR) and adopt kernel approximation with randomized features,
leading to low memory costs and high efficiency in large-scale learning.
Experiments show that **Joker** saves up to 90% memory but achieves comparable training time and performance (or even better) than the state-of-the-art methods.
|
unified optimization, large-scale kernel method, lightweight model
|
We propose a unified framework for kernel methods (not limited to KRR) with low computation requirements.
| 2,004
|
2505.17765
|
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] |
https://github.com/Apple-Zhang/Joker-paper
|
2,701
|
Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges
|
https://openreview.net/forum?id=QAkh7c3r7c
|
[
"Alex Crane",
"Thomas Stanley",
"Blair D. Sullivan",
"Nate Veldt"
] |
Poster
|
We consider a framework for clustering edge-colored hypergraphs, where the goal is to cluster (equivalently, to *color*) objects based on the primary type of multiway interactions they participate in. One well-studied objective is to color nodes to minimize the number of *unsatisfied* hyperedges---those containing one or more nodes whose color does not match the hyperedge color. We motivate and present advances for several directions that extend beyond this minimization problem. We first provide new algorithms for maximizing *satisfied* edges, which is the same at optimality but is much more challenging to approximate, with all prior work restricted to graphs. We develop the first approximation algorithm for hypergraphs, and then refine it to improve the best-known approximation factor for graphs. We then introduce new objective functions that incorporate notions of balance and fairness, and provide new hardness results, approximations, and fixed-parameter tractability results.
|
hypergraph clustering, edge-colored clustering, approximation algorithms, parameterized complexity
|
We provide improved approximation algorithms and parameterized complexity results for clustering edge-colored hypergraphs
| 1,996
|
2502.13000
|
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] |
https://github.com/tommy1019/AltECC
|
2,702
|
Sparsing Law: Towards Large Language Models with Greater Activation Sparsity
|
https://openreview.net/forum?id=SBUc5wirM8
|
[
"Yuqi Luo",
"Chenyang Song",
"Xu Han",
"Yingfa Chen",
"Chaojun Xiao",
"Xiaojun Meng",
"Liqun Deng",
"Jiansheng Wei",
"Zhiyuan Liu",
"Maosong Sun"
] |
Poster
|
Activation sparsity denotes the existence of substantial weakly-contributed neurons within feed-forward networks of large language models (LLMs), providing wide potential benefits such as computation acceleration. However, existing works lack thorough quantitative studies on this useful property, in terms of both its measurement and influential factors. In this paper, we address three underexplored research questions: (1) How can activation sparsity be measured more accurately? (2) How is activation sparsity affected by the model architecture and training process? (3) How can we build a more sparsely activated and efficient LLM? Specifically, we develop a generalizable and performance-friendly metric, named CETT-PPL-1\%, to measure activation sparsity. Based on CETT-PPL-1\%, we quantitatively study the influence of various factors and observe several important phenomena, such as the convergent power-law relationship between sparsity and training data amount, the higher competence of ReLU activation than mainstream SiLU activation, the potential sparsity merit of a small width-depth ratio, and the scale insensitivity of activation sparsity. Finally, we provide implications for building sparse and effective LLMs, and demonstrate the reliability of our findings by training a 2.4B model with a sparsity ratio of 93.52\%, showing 4.1$\times$ speedup compared with its dense version. The codes and checkpoints are available at https://github.com/thunlp/SparsingLaw/.
|
activation sparsity, large language model
|
Comprensive studies on the measurement, influential factors, better architectural design and training practice for activation sparsity.
| 1,995
|
2411.02335
|
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] |
https://github.com/thunlp/SparsingLaw
|
2,703
|
Measuring Diversity in Synthetic Datasets
|
https://openreview.net/forum?id=DMJ3b19RAJ
|
[
"Yuchang Zhu",
"Huizhe Zhang",
"Bingzhe Wu",
"Jintang Li",
"Zibin Zheng",
"Peilin Zhao",
"Liang Chen",
"Yatao Bian"
] |
Poster
|
Large language models (LLMs) are widely adopted to generate synthetic datasets for various natural language processing (NLP) tasks, such as text classification and summarization. However, accurately measuring the diversity of these synthetic datasets—an aspect crucial for robust model performance—remains a significant challenge. In this paper, we introduce DCScore, a novel method for measuring synthetic dataset diversity from a classification perspective. Specifically, DCScore formulates diversity evaluation as a sample classification task, leveraging mutual relationships among samples. We further provide theoretical verification of the diversity-related axioms satisfied by DCScore, highlighting its role as a principled diversity evaluation method. Experimental results on synthetic datasets reveal that DCScore enjoys a stronger correlation with multiple diversity pseudo-truths of evaluated datasets, underscoring its effectiveness. Moreover, both empirical and theoretical evidence demonstrate that DCScore substantially reduces computational costs compared to existing methods. Code is available at: https://github.com/bluewhalelab/dcscore.
|
diversity evaluation, synthetic data, LLMs
|
We propose a novel method to evaluate synthetic dataset diversity from a classification perspective.
| 1,994
|
2502.08512
|
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] |
https://github.com/bluewhalelab/dcscore
|
2,704
|
Plausible Token Amplification for Improving Accuracy of Differentially Private In-Context Learning Based on Implicit Bayesian Inference
|
https://openreview.net/forum?id=skAjaAEuA2
|
[
"Yusuke Yamasaki",
"Kenta Niwa",
"Daiki Chijiwa",
"Takumi Fukami",
"Takayuki Miura"
] |
Poster
|
We propose Plausible Token Amplification (PTA) to improve the accuracy of Differentially Private In-Context Learning (DP-ICL) using DP synthetic demonstrations. While Tang et al. empirically improved the accuracy of DP-ICL by limiting vocabulary space during DP synthetic demonstration generation, its theoretical basis remains unexplored. By interpreting ICL as implicit Bayesian inference on a concept underlying demonstrations, we not only provide theoretical evidence supporting Tang et al.'s empirical method but also introduce PTA, a refined method for modifying next-token probability distribution. Through the modification, PTA highlights tokens that distinctly represent the ground-truth concept underlying the original demonstrations. As a result, generated DP synthetic demonstrations guide the Large Language Model to successfully infer the ground-truth concept, which improves the accuracy of DP-ICL. Experimental evaluations on both synthetic and real-world text-classification datasets validated the effectiveness of PTA.
|
in-context learning, differential privacy, large language models
|
We propose Plausible Token Amplification (PTA), the first theoretically grounded method to improve the accuracy of DP-ICL by highlighting the task-relevant tokens.
| 1,991
| null |
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] |
https://github.com/Yusuke-Yamasaki/pta
|
2,705
|
Think Twice, Act Once: A Co-Evolution Framework of LLM and RL for Large-Scale Decision Making
|
https://openreview.net/forum?id=ySWrJer7mW
|
[
"Xu Wan",
"Wenyue Xu",
"Chao Yang",
"Mingyang Sun"
] |
Poster
|
Recent advancements in Large Language Models (LLMs) and Reinforcement Learning (RL) have shown significant promise in decision-making tasks. Nevertheless, for large-scale industrial decision problems, both approaches face distinct challenges: LLMs lack real-time long-sequence decision-making capabilities, while RL struggles with sample efficiency in vast action spaces. To bridge this gap, we propose **A**gents **C**o-**E**volution (ACE), a synergistic framework between LLMs and RL agent for large-scale decision-making scenarios. ACE introduces a dual-role trajectory refinement mechanism where LLMs act as both Policy Actor and Value Critic during RL's training: the Actor refines suboptimal actions via multi-step reasoning and environment validation, while the Critic performs temporal credit assignment through trajectory-level reward shaping. Concurrently, RL agent enhance LLMs' task-specific decision-making via prioritized experience replay.
Through extensive experiments across multiple power grid operation challenges with action spaces exceeding 60K discrete actions, ACE demonstrates superior performance over existing RL methods and LLM-based methods.
|
Power Grid Control, Large Language Models-enhanced Reinforcement Learning
|
We present a co-evolution framework where large language models and reinforcement learning agents enhance each other's capabilities to achieve efficient learning and rapid execution in large-scale numerical decision-making.
| 1,981
|
2506.02522
|
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] | null |
2,706
|
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
|
https://openreview.net/forum?id=qbIcZLSvmH
|
[
"Rudrajit Das",
"Inderjit S Dhillon",
"Alessandro Epasto",
"Adel Javanmard",
"Jieming Mao",
"Vahab Mirrokni",
"Sujay Sanghavi",
"Peilin Zhong"
] |
Poster
|
The performance of a model trained with noisy labels is often improved by simply *retraining* the model with its *own predicted hard labels* (i.e., $1$/$0$ labels). Yet, a detailed theoretical characterization of this phenomenon is lacking. In this paper, we theoretically analyze retraining in a linearly separable binary classification setting with randomly corrupted labels given to us and prove that retraining can improve the population accuracy obtained by initially training with the given (noisy) labels. To the best of our knowledge, this is the first such theoretical result. Retraining finds application in improving training with local label differential privacy (DP), which involves training with noisy labels. We empirically show that retraining selectively on the samples for which the predicted label matches the given label significantly improves label DP training at no extra privacy cost; we call this consensus-based retraining. For example, when training ResNet-18 on CIFAR-100 with $\epsilon=3$ label DP, we obtain more than $6$% improvement in accuracy with consensus-based retraining.
|
Retraining, Predicted Labels, Hard Labels, Label Noise, Label DP
|
We theoretically show that retraining a model with its predicted hard labels can improve its accuracy when the given labels are noisy, and empirically demonstrate that retraining significantly improves label DP training at no extra privacy cost.
| 1,980
|
2406.11206
|
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] | null |
2,707
|
Self-Supervised Learning of Intertwined Content and Positional Features for Object Detection
|
https://openreview.net/forum?id=Ku2fvCEpwx
|
[
"Kang-Jun Liu",
"Masanori Suganuma",
"Takayuki Okatani"
] |
Poster
|
We present a novel self-supervised feature learning method using Vision Transformers (ViT) as the backbone, specifically designed for object detection and instance segmentation. Our approach addresses the challenge of extracting features that capture both class and positional information, which are crucial for these tasks. The method introduces two key components: (1) a positional encoding tied to the cropping process in contrastive learning, which utilizes a novel vector field representation for positional embeddings; and (2) masking and prediction, similar to conventional Masked Image Modeling (MIM), applied in parallel to both content and positional embeddings of image patches. These components enable the effective learning of intertwined content and positional features. We evaluate our method against state-of-the-art approaches, pre-training on ImageNet-1K and fine-tuning on downstream tasks. Our method outperforms the state-of-the-art SSL methods on the COCO object detection benchmark, achieving significant improvements with fewer pre-training epochs. These results suggest that better integration of positional information into self-supervised learning can improve performance on the dense prediction tasks.
|
Self-supervised learning; Instance segmentation pre-training; Object detection pre-training; Vision transformer
| null | 1,974
| null |
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] |
https://github.com/KJ-rc/IntertwinedSSL
|
2,708
|
Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training
|
https://openreview.net/forum?id=fbmj0EoeFk
|
[
"Minghao Xu",
"Jiaze Song",
"Keming Wu",
"Xiangxin Zhou",
"Bin CUI",
"Wentao Zhang"
] |
Poster
|
Understanding the various properties of glycans with machine learning has shown some preliminary promise. However, previous methods mainly focused on modeling the backbone structure of glycans as graphs of monosaccharides (i.e., sugar units), while they neglected the atomic structures underlying each monosaccharide, which are actually important indicators of glycan properties. We fill this blank by introducing the GlycanAA model for All-Atom-wise Glycan modeling. GlycanAA models a glycan as a heterogeneous graph with monosaccharide nodes representing its global backbone structure and atom nodes representing its local atomic-level structures. Based on such a graph, GlycanAA performs hierarchical message passing to capture from local atomic-level interactions to global monosaccharide-level interactions. To further enhance model capability, we pre-train GlycanAA on a high-quality unlabeled glycan dataset, deriving the PreGlycanAA model. We design a multi-scale mask prediction algorithm to endow the model about different levels of dependencies in a glycan. Extensive benchmark results show the superiority of GlycanAA over existing glycan encoders and verify the further improvements achieved by PreGlycanAA. We maintain all resources at https://github.com/kasawa1234/GlycanAA.
|
Glycan Machine Learning, Heterogeneous Graph Modeling, Self-Supervised Pre-training
|
This work proposes an all-atom-wise glycan encoder GlycanAA and a pre-trained version of it PreGlycanAA.
| 1,967
|
2506.01376
|
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] |
https://github.com/kasawa1234/GlycanAA
|
2,709
|
DVI:A Derivative-based Vision Network for INR
|
https://openreview.net/forum?id=4Xnqm4f71y
|
[
"Runzhao Yang",
"Xiaolong Wu",
"Zhihong Zhang",
"Fabian Zhang",
"Tingxiong Xiao",
"Zongren Li",
"Kunlun He",
"Jinli Suo"
] |
Poster
|
Recent advancements in computer vision have seen Implicit Neural Representations (INR) becoming a dominant representation form for data due to their compactness and expressive power. To solve various vision tasks with INR data, vision networks can either be purely INR-based, but are thereby limited by simplistic operations and performance constraints, or include raster-based methods, which then tend to lose crucial structural information of the INR during the conversion process. To address these issues, we propose DVI, a novel Derivative-based Vision network for INR, capable of handling a variety of vision tasks across various data modalities, while achieving the best performance among the existing methods by incorporating state of the art raster-based methods into a INR based architecture. DVI excels by extracting semantic information from the high order derivative map of the INR, then seamlessly fusing it into a pre-existing raster-based vision network, enhancing its performance with deeper, task-relevant semantic insights. Extensive experiments on five vision tasks across three data modalities demonstrate DVI's superiority over existing methods. Additionally, our study encompasses comprehensive ablation studies to affirm the efficacy of each element of DVI, the influence of different derivative computation techniques and the impact of derivative orders. Reproducible codes are provided in the supplementary materials.
|
Data Compression, Implicit Neural Representation
|
we propose DVI, a novel Derivative-based Vision network for INR, capable of handling a variety of vision tasks across various data modalities
| 1,966
| null |
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] | null |
2,710
|
Variational Rectified Flow Matching
|
https://openreview.net/forum?id=Rk18ZikrFI
|
[
"Pengsheng Guo",
"Alex Schwing"
] |
Poster
|
We study Variational Rectified Flow Matching, a framework that enhances classic rectified flow matching by modeling multi-modal velocity vector-fields. At inference time, classic rectified flow matching 'moves' samples from a source distribution to the target distribution by solving an ordinary differential equation via integration along a velocity vector-field. At training time, the velocity vector-field is learnt by linearly interpolating between coupled samples one drawn from the source and one drawn from the target distribution randomly. This leads to ''ground-truth'' velocity vector-fields that point in different directions at the same location, i.e., the velocity vector-fields are multi-modal/ambiguous. However, since training uses a standard mean-squared-error loss, the learnt velocity vector-field averages ''ground-truth'' directions and isn't multi-modal. In contrast, variational rectified flow matching learns and samples from multi-modal flow directions. We show on synthetic data, MNIST, CIFAR-10, and ImageNet that variational rectified flow matching leads to compelling results.
|
Flow Matching, Diffusion Model, Generative Model
|
We study Variational Rectified Flow Matching, a framework that improves classic rectified flow matching by modeling multi-modal velocity vector fields, and demonstrate its compelling results on synthetic data, MNIST, CIFAR-10, and ImageNet.
| 1,962
|
2502.09616
|
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] | null |
2,711
|
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning
|
https://openreview.net/forum?id=drBVowFvqf
|
[
"Dongsu Lee",
"Minhae Kwon"
] |
Poster
|
The goal of offline reinforcement learning (RL) is to extract the best possible policy from the previously collected dataset considering the *out-of-distribution* (OOD) sample issue. Offline model-based RL (MBRL) is a captivating solution capable of alleviating such issues through a \textit{state-action transition augmentation} with a learned dynamic model. Unfortunately, offline MBRL methods have been observed to fail in sparse rewarded and long-horizon environments for a long time. In this work, we propose a novel MBRL method, dubbed Temporal Distance-Aware Transition Augmentation (TempDATA), that generates additional transitions in a geometrically structured representation space, instead of state space. For comprehending long-horizon behaviors efficiently, our main idea is to learn state abstraction, which captures a *temporal distance* from both *trajectory and transition levels* of state space. Our experiments empirically confirm that TempDATA outperforms previous offline MBRL methods and achieves matching or surpassing the performance of diffusion-based trajectory augmentation and goal-conditioned RL on the D4RL AntMaze, FrankaKitchen, CALVIN, and pixel-based FrankaKitchen.
|
State Representation, Latent World Model, Data Augmentation
| null | 1,959
|
2505.13144
|
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] | null |
2,712
|
Variational Counterfactual Intervention Planning to Achieve Target Outcomes
|
https://openreview.net/forum?id=ggyHPOXLGH
|
[
"Xin Wang",
"Shengfei Lyu",
"Chi Luo",
"Xiren Zhou",
"Huanhuan Chen"
] |
Poster
|
A key challenge in personalized healthcare is identifying optimal intervention sequences to guide temporal systems toward target outcomes, a novel problem we formalize as counterfactual target achievement. In addressing this problem, directly adopting counterfactual estimation methods face compounding errors due to the unobservability of counterfactuals. To overcome this, we propose Variational Counterfactual Intervention Planning (VCIP), which reformulates the problem by modeling the conditional likelihood of achieving target outcomes, implemented through variational inference. By leveraging the g-formula to bridge the gap between interventional and observational log-likelihoods, VCIP enables reliable training from observational data. Experiments on both synthetic and real-world datasets show that VCIP significantly outperforms existing methods in target achievement accuracy.
|
causal inference, counterfactual reasoning, counterfactual target achievement
|
We introduce a novel counterfactual target achievement problem in temporal systems and propose VCIP to find optimal interventions by modeling achievement probability.
| 1,947
| null |
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] | null |
2,713
|
Generation from Noisy Examples
|
https://openreview.net/forum?id=ouaiJlE1hJ
|
[
"Ananth Raman",
"Vinod Raman"
] |
Poster
|
We continue to study the learning-theoretic foundations of generation by extending the results from Kleinberg and Mullainathan [2024] and Li et al. [2024] to account for noisy example streams. In the noiseless setting of Kleinberg and Mullainathan [2024] and Li et al. [2024], an adversary picks a hypothesis from a binary hypothesis class and provides a generator with a sequence of its positive examples. The goal of the generator is to eventually output new, unseen positive examples. In the noisy setting, an adversary still picks a hypothesis and a sequence of its positive examples. But, before presenting the stream to the generator, the adversary inserts a finite number of negative examples. Unaware of which examples are noisy, the goal of the generator is to still eventually output new, unseen positive examples. In this paper, we provide necessary and sufficient conditions for when a binary hypothesis class can be noisily generatable. We provide such conditions with respect to various constraints on the number of distinct examples that need to be seen before perfect generation of positive examples. Interestingly, for finite and countable classes we show that generatability is largely unaffected by the presence of a finite number of noisy examples.
|
Learning Theory, Language Generation, Generative Machine Learning
|
We continue to study the learning-theoretic foundations of generation by extending the results from Kleinberg and Mullainathan [2024] and Li et al. [2024] to account for noisy example streams.
| 1,936
|
2501.04179
|
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] | null |
2,714
|
Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions
|
https://openreview.net/forum?id=9MJ8FpUvjD
|
[
"Aleksandr Kolomeitsev",
"ANH-HUY PHAN"
] |
Poster
|
Topology optimization plays a crucial role in designing efficient and manufacturable structures. Traditional methods often yield free-form voids that, although providing design flexibility, introduce significant manufacturing challenges and require extensive post-processing. Conversely, feature-mapping topology optimization reduces post-processing efforts by constructing topologies using predefined geometric features. Nevertheless, existing approaches are significantly constrained by the limited set of geometric features available, the variety of parameters that each type of geometric feature can possess, and the necessity of employing differentiable signed distance functions. In this paper, we present a novel method that combines Neural Heaviside Signed Distance Functions (Heaviside SDFs) with structured latent shape representations to generate manufacturable voids directly within the optimization framework. Our architecture incorporates encoder and decoder networks to effectively approximate the Heaviside function and facilitate optimization within a unified latent space, thus addressing the feature diversity limitations of current feature-mapping techniques. Experimental results validate the effectiveness of our approach in balancing structural compliance, offering a new pathway to CAD-integrated design with minimal human intervention.
|
Neural Heaviside Signed Distance Functions, Autoencoder Architectures, Topology Optimization
|
Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions
| 1,932
| null |
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] |
https://github.com/Alexander19970212/NHSDF-TOp
|
2,715
|
SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer
|
https://openreview.net/forum?id=27hOkXzy9e
|
[
"Enze Xie",
"Junsong Chen",
"Yuyang Zhao",
"Jincheng YU",
"Ligeng Zhu",
"Yujun Lin",
"Zhekai Zhang",
"Muyang Li",
"Junyu Chen",
"Han Cai",
"Bingchen Liu",
"Daquan Zhou",
"Song Han"
] |
Poster
|
This paper presents SANA-1.5, a linear Diffusion Transformer for efficient scaling in text-to-image generation. Building upon SANA-1.0, we introduce three key innovations: (1) Efficient Training Scaling: A depth-growth paradigm that enables scaling from 1.6B to 4.8B parameters with significantly reduced computational resources, combined with a memory-efficient 8-bit optimizer. (2) Model Depth Pruning: A block importance analysis technique for efficient model compression to arbitrary sizes with minimal quality loss. (3) Inference-time Scaling: A repeated sampling strategy that trades computation for model capacity, enabling smaller models to match larger model quality at inference time. Through these strategies, SANA-1.5 achieves a text-image alignment score of 0.72 on GenEval, which can be further improved to 0.80 through inference scaling, establishing a new SoTA on GenEval benchmark. These innovations enable efficient model scaling across different compute budgets while maintaining high quality, making high-quality image generation more accessible.
|
Diffusion Model, Inference Scaling, Linear Attention
|
Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer
| 1,929
|
2501.18427
|
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] |
https://github.com/NVlabs/Sana
|
2,716
|
A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
|
https://openreview.net/forum?id=Wwj6jjxZet
|
[
"Jongha Jon Ryu",
"Abhin Shah",
"Gregory W. Wornell"
] |
Poster
|
This paper studies a family of estimators based on noise-contrastive estimation (NCE) for learning unnormalized distributions. The main contribution of this work is to provide a unified perspective on various methods for learning unnormalized distributions, which have been independently proposed and studied in separate research communities, through the lens of NCE. This unified view offers new insights into existing estimators. Specifically, for exponential families, we establish the finite-sample convergence rates of the proposed estimators under a set of regularity assumptions, most of which are new.
|
unnormalized models, exponential family distributions, noise-contrastive estimation, interactive screening, finite-sample analysis
|
We provide a new, unified view on learning unnormalized distributions via a family of NCE principles, and establish finite-sample rates for the wide class of estimators for exponential family distributions.
| 1,927
|
2409.18209
|
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] | null |
2,717
|
Backdoor Attacks in Token Selection of Attention Mechanism
|
https://openreview.net/forum?id=ZEPWk1Q6ww
|
[
"Yunjuan Wang",
"Raman Arora"
] |
Poster
|
Despite the remarkable success of large foundation models across a range of tasks, they remain susceptible to security threats such as backdoor attacks. By injecting poisoned data containing specific triggers during training, adversaries can manipulate model predictions in a targeted manner. While prior work has focused on empirically designing and evaluating such attacks, a rigorous theoretical understanding of when and why they succeed is lacking. In this work, we analyze backdoor attacks that exploit the token selection process within attention mechanisms--a core component of transformer-based architectures. We show that single-head self-attention transformers trained via gradient descent can interpolate poisoned training data. Moreover, we prove that when the backdoor triggers are sufficiently strong but not overly dominant, attackers can successfully manipulate model predictions. Our analysis characterizes how adversaries manipulate token selection to alter outputs and identifies the theoretical conditions under which these attacks succeed. We validate our findings through experiments on synthetic datasets.
|
Backdoor attacks, attention mechanism, token selection, transformer models, gradient descent dynamics, theoretical analysis, adversarial machine learning, label poisoning
| null | 1,926
| null |
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] | null |
2,718
|
WMAdapter: Adding WaterMark Control to Latent Diffusion Models
|
https://openreview.net/forum?id=xXYGBmpMAj
|
[
"Hai Ci",
"Yiren Song",
"Pei Yang",
"Jinheng Xie",
"Mike Zheng Shou"
] |
Poster
|
Watermarking is essential for protecting the copyright of AI-generated images. We propose WMAdapter, a diffusion model watermark plugin that embeds user-specified watermark information seamlessly during the diffusion generation process. Unlike previous methods that modify diffusion modules to incorporate watermarks, WMAdapter is designed to keep all diffusion components intact, resulting in sharp, artifact-free images. To achieve this, we introduce two key innovations: (1) We develop a contextual adapter that conditions on the content of the cover image to generate adaptive watermark embeddings. (2) We implement an additional finetuning step and a hybrid finetuning strategy that suppresses noticeable artifacts while preserving the integrity of the diffusion components. Empirical results show that WMAdapter provides strong flexibility, superior image quality, and competitive watermark robustness.
|
watermark, latent diffusion model
|
Watermark LDM via a plugin without compromising image quality
| 1,914
|
2406.08337
|
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0.059022560715675354,
0.014018655754625797,
0.018199456855654716,
-0.023630423471331596
] |
https://github.com/showlab/WMAdapter
|
2,719
|
MARGE: Improving Math Reasoning with Guided Exploration
|
https://openreview.net/forum?id=ZM5usuLxur
|
[
"Jingyue Gao",
"Runji Lin",
"Keming Lu",
"Bowen Yu",
"Junyang Lin",
"Jianyu Chen"
] |
Poster
|
Large Language Models (LLMs) exhibit strong potential in mathematical reasoning, yet their effectiveness is often limited by a shortage of high-quality queries.
This limitation necessitates scaling up computational responses through self-generated data, yet current methods struggle due to spurious correlated data caused by ineffective exploration across all reasoning stages.
To address such challenge, we introduce **MARGE**: Improving **Ma**th **R**easoning with **G**uided **E**xploration, a novel method that enhances mathematical reasoning through hit-guided exploration.
MARGE systematically explores intermediate reasoning states derived from self-generated solutions, enabling adequate exploration and improved credit assignment throughout the reasoning process.
Notably, MARGE improves both single-shot accuracy and exploration diversity, mitigating a common trade-off in alignment methods.
These results demonstrate MARGE's effectiveness in enhancing mathematical reasoning capabilities and unlocking the potential of scaling self-generated training data.
|
LLM, Reasoning, Reinforcement Learning, Exploration
| null | 1,912
|
2505.12500
|
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] |
https://github.com/georgao35/MARGE
|
2,720
|
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning
|
https://openreview.net/forum?id=Bv7LUUYOiq
|
[
"Adrià López Escoriza",
"Nicklas Hansen",
"Stone Tao",
"Tongzhou Mu",
"Hao Su"
] |
Poster
|
Long-horizon tasks in robotic manipulation present significant challenges in reinforcement learning (RL) due to the difficulty of designing dense reward functions and effectively exploring the expansive state-action space. However, despite a lack of dense rewards, these tasks often have a multi-stage structure, which can be leveraged to decompose the overall objective into manageable sub-goals. In this work, we propose DEMO³, a framework that exploits this structure for efficient learning from visual inputs. Specifically, our approach incorporates multi-stage dense reward learning, a bi-phasic training scheme, and world model learning into a carefully designed demonstration-augmented RL framework that strongly mitigates the challenge of exploration in long-horizon tasks. Our evaluations demonstrate that our method improves data-efficiency by an average of 40% and by 70% on particularly difficult tasks
compared to state-of-the-art approaches. We validate this across 16 sparse-reward tasks spanning four domains, including challenging humanoid visual control tasks using as few as five demonstrations.
|
Reinforcement Learning, Learning from Demonstrations, Robotics, Manipulation
|
We propose a framework multi-stage manipulation tasks with sparse rewards and visual inputs; our framework combines learned dense rewards, model-based RL, a bi-phasic training scheme, and a small number of demonstrations.
| 1,910
|
2503.01837
|
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] |
https://github.com/adrialopezescoriza/demo3
|
2,721
|
Clone-Robust AI Alignment
|
https://openreview.net/forum?id=vbw9hSlOaG
|
[
"Ariel D. Procaccia",
"Benjamin Schiffer",
"Shirley Zhang"
] |
Poster
|
A key challenge in training Large Language Models (LLMs) is properly aligning them with human preferences. Reinforcement Learning with Human Feedback (RLHF) uses pairwise comparisons from human annotators to train reward functions and has emerged as a popular alignment method. However, input datasets in RLHF can be unbalanced due to adversarial manipulation or inadvertent repetition. Therefore, we want RLHF algorithms to perform well even when the set of alternatives is not uniformly distributed. Drawing on insights from social choice theory, we introduce robustness to approximate clones, a desirable property of RLHF algorithms which requires that adding near-duplicate alternatives does not significantly change the learned reward function. We first demonstrate that the standard RLHF algorithm based on regularized maximum likelihood estimation (MLE) fails to satisfy this property. We then propose the weighted MLE, a new RLHF algorithm that modifies the standard regularized MLE by weighting alternatives based on their similarity to other alternatives. This new algorithm guarantees robustness to approximate clones while preserving desirable theoretical properties.
|
RLHF, AI alignment, social choice
| null | 1,905
|
2501.09254
|
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] | null |
2,722
|
Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
|
https://openreview.net/forum?id=YeQ8SGjarH
|
[
"Haoyang Li",
"Xin Wang",
"Xueling Zhu",
"Weigao Wen",
"Wenwu Zhu"
] |
Poster
|
Graph neural networks (GNNs) have achieved remarkable success, yet most are developed under the in-distribution assumption and fail to generalize to out-of-distribution (OOD) environments. To tackle this problem, some graph invariant learning methods aim to learn invariant subgraph against distribution shifts, which heavily rely on predefined or automatically generated environment labels. However, directly annotating or estimating such environment labels from biased graph data is typically impractical or inaccurate for real-world graphs. Consequently, GNNs may become biased toward variant patterns, resulting in poor OOD generalization. In this paper, we propose to learn disentangled invariant subgraph via self-supervised contrastive variant subgraph estimation for achieving satisfactory OOD generalization. Specifically, we first propose a GNN-based invariant subgraph generator to disentangle the invariant and variant subgraphs. Then, we estimate the degree of the spurious correlations by conducting self-supervised contrastive learning on variant subgraphs. Thanks to the accurate identification and estimation of the variant subgraphs, we can capture invariant subgraphs effectively and further eliminate spurious correlations by inverse propensity score reweighting. We provide theoretical analyses to show that our model can disentangle the ground-truth invariant and variant subgraphs for OOD generalization. Extensive experiments demonstrate the superiority of our model over state-of-the-art baselines.
|
Disentanglement, Graph Neural Network, Distribution Shift
|
We propose to learn disentangled invariant subgraph via self-supervised contrastive variant subgraph estimation for achieving satisfying OOD generalization.
| 1,902
| null |
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] | null |
2,723
|
Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
|
https://openreview.net/forum?id=kONwjsPKcI
|
[
"Rickard Karlsson",
"JH Krijthe"
] |
Poster
|
A major challenge in estimating treatment effects in observational studies is the reliance on untestable conditions such as the assumption of no unmeasured confounding. In this work, we propose an algorithm that can falsify the assumption of no unmeasured confounding in a setting with observational data from multiple heterogeneous sources, which we refer to as environments. Our proposed falsification strategy leverages a key observation that unmeasured confounding can cause observed causal mechanisms to appear dependent. Building on this observation, we develop a novel two-stage procedure that detects these dependencies with high statistical power while controlling false positives. The algorithm does not require access to randomized data and, in contrast to other falsification approaches, functions even under transportability violations when the environment has a direct effect on the outcome of interest. To showcase the practical relevance of our approach, we show that our method is able to efficiently detect confounding on both simulated and semi-synthetic data.
|
causal inference, observational data, falsification, unmeasured confounding, independent causal mechanisms
|
We develop a novel algorithm to falsify the assumption of no unmeasured confounding using multi-environment observational data.
| 1,866
|
2502.06231
|
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] |
https://github.com/RickardKarl/falsification-unconfoundedness
|
2,724
|
Test-Time Learning for Large Language Models
|
https://openreview.net/forum?id=iCYbIaGKSR
|
[
"Jinwu Hu",
"Zitian Zhang",
"Guohao Chen",
"Xutao Wen",
"Chao Shuai",
"Wei Luo",
"Bin Xiao",
"Yuanqing Li",
"Mingkui Tan"
] |
Poster
|
While Large Language Models (LLMs) have exhibited remarkable emergent capabilities through extensive pre-training, they still face critical limitations in generalizing to specialized domains and handling diverse linguistic variations, known as distribution shifts. In this paper, we propose a Test-Time Learning (TTL) paradigm for LLMs, namely TLM, which dynamically adapts LLMs to target domains using only unlabeled test data during testing. Specifically, we first provide empirical evidence and theoretical insights to reveal that more accurate predictions from LLMs can be achieved by minimizing the input perplexity of the unlabeled test data. Based on this insight, we formulate the Test-Time Learning process of LLMs as input perplexity minimization, enabling self-supervised enhancement of LLM performance. Furthermore, we observe that high-perplexity samples tend to be more informative for model optimization. Accordingly, we introduce a Sample Efficient Learning Strategy that actively selects and emphasizes these high-perplexity samples for test-time updates. Lastly, to mitigate catastrophic forgetting and ensure adaptation stability, we adopt Low-Rank Adaptation (LoRA) instead of full-parameter optimization, which allows lightweight model updates while preserving more original knowledge from the model. We introduce the AdaptEval benchmark for TTL and demonstrate through experiments that TLM improves performance by at least 20% compared to original LLMs on domain knowledge adaptation.
|
Large Language Models, Test-Time Learning
|
We propose a Test-Time Learning paradigm for Large Language Models that dynamically adapts them to target domains using only unlabeled test data.
| 1,861
|
2505.20633
|
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] |
https://github.com/Fhujinwu/TLM
|
2,725
|
Learning Progress Driven Multi-Agent Curriculum
|
https://openreview.net/forum?id=Al6qG8BlKg
|
[
"Wenshuai Zhao",
"Zhiyuan Li",
"Joni Pajarinen"
] |
Poster
|
The number of agents can be an effective curriculum variable for controlling the difficulty of multi-agent reinforcement learning (MARL) tasks. Existing work typically uses manually defined curricula such as linear schemes. We identify two potential flaws while applying existing reward-based automatic curriculum learning methods in MARL: (1) The expected episode return used to measure task difficulty has high variance; (2) Credit assignment difficulty can be exacerbated in tasks where increasing the number of agents yields higher returns which is common in many MARL tasks. To address these issues, we propose to control the curriculum by using a TD-error based *learning progress* measure and by letting the curriculum proceed from an initial context distribution to the final task specific one. Since our approach maintains a distribution over the number of agents and measures learning progress rather than absolute performance, which often increases with the number of agents, we alleviate problem (2). Moreover, the learning progress measure naturally alleviates problem (1) by aggregating returns. In three challenging sparse-reward MARL benchmarks, our approach outperforms state-of-the-art baselines.
|
Curriculum learning, Temporal difference, Sparse reward
|
We propose learning progress based self-paced curriculum learning for MARL tasks, addressing issues of existing return-based curriulum learning methods when applied to MARL.
| 1,858
|
2205.10016
|
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] |
https://github.com/wenshuaizhao/spmarl
|
2,726
|
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
|
https://openreview.net/forum?id=QRNpmG8XGd
|
[
"Filip Ekström Kelvinius",
"Zheng Zhao",
"Fredrik Lindsten"
] |
Poster
|
A recent line of research has exploited pre-trained generative diffusion models as priors for solving Bayesian inverse problems. We contribute to this research direction by designing a sequential Monte Carlo method for linear-Gaussian inverse problems which builds on ``decoupled diffusion", where the generative process is designed such that larger updates to the sample are possible. The method is asymptotically exact and we demonstrate the effectiveness of our Decoupled Diffusion Sequential Monte Carlo (DDSMC) algorithm on both synthetic as well as protein and image data. Further, we demonstrate how the approach can be extended to discrete data.
|
diffusion models, sequential monte carlo, posterior sampling
|
Sequential Monte Carlo method for solving linear-Gaussian inverse problems
| 1,829
|
2502.06379
|
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] |
https://github.com/filipekstrm/ddsmc
|
2,727
|
QT-DoG: Quantization-Aware Training for Domain Generalization
|
https://openreview.net/forum?id=OS2ZVeHI4U
|
[
"Saqib Javed",
"Hieu Le",
"Mathieu Salzmann"
] |
Poster
|
A key challenge in Domain Generalization (DG) is preventing overfitting to source domains, which can be mitigated by finding flatter minima in the loss landscape. In this work, we propose Quantization-aware Training for Domain Generalization (QT-DoG) and demonstrate that weight quantization effectively leads to flatter minima in the loss landscape, thereby enhancing domain generalization. Unlike traditional quantization methods focused on model compression, QT-DoG exploits quantization as an implicit regularizer by inducing noise in model weights, guiding the optimization process toward flatter minima that are less sensitive to perturbations and overfitting. We provide both an analytical perspective and empirical evidence demonstrating that quantization inherently encourages flatter minima, leading to better generalization across domains. Moreover, with the benefit of reducing the model size through quantization, we demonstrate that an ensemble of multiple quantized models further yields superior accuracy than the state-of-the-art DG approaches with no computational or memory overheads. Code is released at: https://saqibjaved1.github.io/QT_DoG/.
|
Domain Generalization, Quantization, Ensemble, Network Compression, Flat Minima, Regularization
|
QT-DoG leverages weight quantization to promote flatter minima, enhancing generalization across unseen domains while reducing model size and computational costs.
| 1,828
| null |
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] | null |
2,728
|
MaskTwins: Dual-form Complementary Masking for Domain-Adaptive Image Segmentation
|
https://openreview.net/forum?id=9CpeZ8BzPO
|
[
"Jiawen Wang",
"Yinda Chen",
"Xiaoyu Liu",
"Che Liu",
"Dong Liu",
"Jianqing Gao",
"Zhiwei Xiong"
] |
Poster
|
Recent works have correlated Masked Image Modeling (MIM) with consistency regularization in Unsupervised Domain Adaptation (UDA). However, they merely treat masking as a special form of deformation on the input images and neglect the theoretical analysis, which leads to a superficial understanding of masked reconstruction and insufficient exploitation of its potential in enhancing feature extraction and representation learning. In this paper, we reframe masked reconstruction as a sparse signal reconstruction problem and theoretically prove that the dual form of complementary masks possesses superior capabilities in extracting domain-agnostic image features. Based on this compelling insight, we propose MaskTwins, a simple yet effective UDA framework that integrates masked reconstruction directly into the main training pipeline. MaskTwins uncovers intrinsic structural patterns that persist across disparate domains by enforcing consistency between predictions of images masked in complementary ways, enabling domain generalization in an end-to-end manner. Extensive experiments verify the superiority of MaskTwins over baseline methods in natural and biological image segmentation.
These results demonstrate the significant advantages of MaskTwins in extracting domain-invariant features without the need for separate pre-training, offering a new paradigm for domain-adaptive segmentation. The source code is available at https://github.com/jwwang0421/masktwins.
|
domain adaptation, unsupervised learning, masked image modeling, semantic segmentation, complementary masking
| null | 1,826
| null |
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] |
https://github.com/jwwang0421/masktwins
|
2,729
|
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
|
https://openreview.net/forum?id=EiM163eZyg
|
[
"Junbo Yin",
"Chao Zha",
"Wenjia He",
"Chencheng Xu",
"Xin Gao"
] |
Poster
|
Existing PLMs generate protein sequences based on a single-condition constraint from a specific modality, struggling to simultaneously satisfy multiple constraints across different modalities. In this work, we introduce CFP-GEN, a novel diffusion language model for Combinatorial Functional Protein GENeration. CFP-GEN facilitates the de novo protein design by integrating multimodal conditions with functional, sequence, and structural constraints. Specifically, an Annotation-Guided Feature Modulation (AGFM) module is introduced to dynamically adjust the protein feature distribution based on composable functional annotations, e.g., GO terms, IPR domains and EC numbers. Meanwhile, the ResidueControlled Functional Encoding (RCFE) module captures residue-wise interaction to ensure more precise control. Additionally, off-the-shelf 3D structure encoders can be seamlessly integrated to impose geometric constraints. We demonstrate that CFP-GEN enables high-throughput generation of novel proteins with functionality comparable to natural proteins, while achieving a high success rate in designing multifunctional proteins.
|
function, protein design, diffusion model, multi-objective
|
A diffusion model capable of designing functional proteins comparable to natural proteins.
| 1,823
| null |
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] |
https://github.com/yinjunbo/cfpgen
|
2,730
|
Distributed Event-Based Learning via ADMM
|
https://openreview.net/forum?id=qLwQuLQHwL
|
[
"Guner Dilsad ER",
"Sebastian Trimpe",
"Michael Muehlebach"
] |
Poster
|
We consider a distributed learning problem, where agents minimize a global objective function by exchanging information over a network. Our approach has two distinct features: (i) It substantially reduces communication by triggering communication only when necessary, and (ii) it is agnostic to the data-distribution among the different agents. We can therefore guarantee convergence even if the local data-distributions of the agents are arbitrarily distinct. We analyze the convergence rate of the algorithm both in convex and nonconvex settings and derive accelerated convergence rates in a convex setting. We also characterize the effect of communication failures and demonstrate that our algorithm is robust to communication failures. The article concludes by presenting numerical results from distributed learning tasks on the MNIST and CIFAR-10 datasets. The experiments underline communication savings of 35\% or more due to the event-based communication strategy, show resilience towards heterogeneous data-distributions, and highlight that our approach outperforms common baselines such as FedAvg, FedProx, SCAFFOLD and FedADMM.
|
distributed learning, event-based optimization, dynamical systems, communication efficiency, heterogeneous data-distribution
|
Our distributed learning method reduces communication cost, handles non-i.i.d. data distributions, ensures fast convergence, and is robust to failures, outperforming standard baselines.
| 1,817
|
2405.10618
|
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] |
https://github.com/guner-dilsad-er/Distributed-Event-Based-ADMM
|
2,731
|
SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics
|
https://openreview.net/forum?id=XWC7JXHXvo
|
[
"Qingtian Zhu",
"Yumin Zheng",
"Yuling Sang",
"Yifan Zhan",
"Ziyan Zhu",
"Jun Ding",
"Yinqiang Zheng"
] |
Poster
|
Spatial Transcriptomics (ST) is a method that captures gene expression profiles aligned with spatial coordinates. The discrete spatial distribution and the super-high dimensional sequencing results make ST data challenging to be modeled effectively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approximation capability of Implicit Neural Representations (INRs) that can enhance both the spatial density and the gene expression. Concretely within the proposed SUICA, we incorporate a graph-augmented Autoencoder to effectively model the context information of the unstructured spots and provide informative embeddings that are structure-aware for spatial mapping. We also tackle the extremely skewed distribution in a regression-by-classification fashion and enforce classification-based loss functions for the optimization of SUICA. By extensive experiments of a wide range of common ST platforms under varying degradations, SUICA outperforms both conventional INR variants and SOTA methods regarding numerical fidelity, statistical correlation, and bio-conservation. The prediction by SUICA also showcases amplified gene signatures that enriches the bio-conservation of the raw data and benefits subsequent analysis.
|
Spatial Transcriptomics, Implicit Neural Representations
|
SUICA = Spatial Transcriptomics + Implicit Neural Representations → Restored Authenticity + Enriched Bio-conservation
| 1,814
|
2412.01124
|
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] |
https://github.com/Szym29/SUICA
|
2,732
|
AlphaQCM: Alpha Discovery in Finance with Distributional Reinforcement Learning
|
https://openreview.net/forum?id=3sXMHlhBSs
|
[
"Zhoufan Zhu",
"Ke Zhu"
] |
Poster
|
For researchers and practitioners in finance, finding synergistic formulaic alphas is very important but challenging. In this paper, we reconsider the discovery of synergistic formulaic alphas from the viewpoint of sequential decision-making, and conceptualize the entire alpha discovery process as a non-stationary and reward-sparse Markov decision process. To overcome the challenges of non-stationarity and reward-sparsity, we propose the AlphaQCM method, a novel distributional reinforcement learning method designed to search for synergistic formulaic alphas efficiently. The AlphaQCM method first learns the Q function and quantiles via a Q network and a quantile network, respectively. Then, the AlphaQCM method applies the quantiled conditional moment method to learn unbiased variance from the potentially biased quantiles. Guided by the learned Q function and variance, the AlphaQCM method navigates the non-stationarity and reward-sparsity to explore the vast search space of formulaic alphas with high efficacy. Empirical applications to real-world datasets demonstrate that our AlphaQCM method significantly outperforms its competitors, particularly when dealing with large datasets comprising numerous stocks.
|
Computational Finance, Formulaic Alpha, Distributional Reinforcement Learning, Quantiled Conditional Moments, Stock Trend Forecasting
| null | 1,805
| null |
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] |
https://github.com/ZhuZhouFan/AlphaQCM
|
2,733
|
Habitizing Diffusion Planning for Efficient and Effective Decision Making
|
https://openreview.net/forum?id=XIxcK2Jzpi
|
[
"Haofei Lu",
"Yifei Shen",
"Dongsheng Li",
"Junliang Xing",
"Dongqi Han"
] |
Poster
|
Diffusion models have shown great promise in decision-making, also known as diffusion planning. However, the slow inference speeds limit their potential for broader real-world applications.
Here, we introduce **Habi**, a general framework that transforms powerful but slow diffusion planning models into fast decision-making models, which mimics the cognitive process in the brain that costly goal-directed behavior gradually transitions to efficient habitual behavior with repetitive practice.
Even using a laptop CPU, the habitized model can achieve an average **800+ Hz** decision-making frequency (faster than previous diffusion planners by orders of magnitude) on standard offline reinforcement learning benchmarks D4RL, while maintaining comparable or even higher performance compared to its corresponding diffusion planner. Our work proposes a fresh perspective of leveraging powerful diffusion models for real-world decision-making tasks.
We also provide robust evaluations and analysis, offering insights from both biological and engineering perspectives for efficient and effective decision-making.
|
Offline Reinforcement Learning, Diffusion Planning, Variational Bayes, Diffusion Models
|
A general framework for habitizing diffusion planning for efficient decision making
| 1,793
|
2502.06401
|
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] | null |
2,734
|
Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound
|
https://openreview.net/forum?id=suZ1pNdKrV
|
[
"David Boetius",
"Stefan Leue",
"Tobias Sutter"
] |
Poster
|
Probabilistic verification problems of neural networks are concerned with formally analysing the output distribution of a neural network under a probability distribution of the inputs. Examples of probabilistic verification problems include verifying the demographic parity fairness notion or quantifying the safety of a neural network. We present a new algorithm for solving probabilistic verification problems of neural networks based on an algorithm for computing and iteratively refining lower and upper bounds on probabilities over the outputs of a neural network. By applying state-of-the-art bound propagation and branch and bound techniques from non-probabilistic neural network verification, our algorithm significantly outpaces existing probabilistic verification algorithms, reducing solving times for various benchmarks from the literature from tens of minutes to tens of seconds. Furthermore, our algorithm compares favourably even to dedicated algorithms for restricted probabilistic verification problems. We complement our empirical evaluation with a theoretical analysis, proving that our algorithm is sound and, under mildly restrictive conditions, also complete when using a suitable set of heuristics.
|
neural network verification, probabilistic verification, fairness, safety, branch and bound
|
A sound and complete branch and bound algorithm for solving probabilistic verification problems of neural networks significantly outpacing existing algorithms.
| 1,788
|
2405.17556
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] |
https://github.com/sen-uni-kn/probspecs
|
2,735
|
Heterogeneous Label Shift: Theory and Algorithm
|
https://openreview.net/forum?id=BY3SSEVcjV
|
[
"Chao Xu",
"Xijia Tang",
"Chenping Hou"
] |
Poster
|
In open-environment applications, data are often collected from heterogeneous modalities with distinct encodings, resulting in feature space heterogeneity. This heterogeneity inherently induces label shift, making cross-modal knowledge transfer particularly challenging when the source and target data exhibit simultaneous heterogeneous feature spaces and shifted label distributions. Existing studies address only partial aspects of this issue, leaving the broader problem unresolved. To bridge this gap, we introduce a new concept of Heterogeneous Label Shift (HLS), targeting this critical but underexplored challenge. We first analyze the impact of heterogeneous feature spaces and label distribution shifts on model generalization and introduce a novel error decomposition theorem. Based on these insights, we propose a bound minimization HLS framework that decouples and tackles feature heterogeneity and label shift accordingly. Extensive experiments on various benchmarks for cross-modal classification validate the effectiveness and practical relevance of the proposed approach.
|
Open environment; Heterogeneous domain adaptation; Label shift; Generalization theory.
|
Research on Heterogeneous Cross-modal Knowledge Transfer in Open Scenarios.
| 1,770
| null |
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] | null |
2,736
|
Mitigating Local Cohesion and Global Sparseness in Graph Contrastive Learning with Fuzzy Boundaries
|
https://openreview.net/forum?id=1b94nzUAvt
|
[
"Yuena Lin",
"Haichun Cai",
"Jun-Yi Hang",
"Haobo Wang",
"Zhen Yang",
"Gengyu Lyu"
] |
Poster
|
Graph contrastive learning (GCL) aims at narrowing positives while dispersing negatives, often causing a minority of samples with great similarities to gather as a small group. It results in two latent shortcomings in GCL: 1) **local cohesion** that a class cluster contains numerous independent small groups, and 2) **global sparseness** that these small groups (or isolated samples) dispersedly distribute among all clusters. These shortcomings make the learned distribution *only focus on local similarities among partial samples, which hinders the ability to capture the ideal global structural properties among real clusters, especially high intra-cluster compactness and inter-cluster separateness*. Considering this, we design a novel fuzzy boundary by extending the original cluster boundary with fuzzy set theory, which involves fuzzy boundary construction and fuzzy boundary contraction to address these shortcomings. The fuzzy boundary construction dilates the original boundaries to bridge the local groups, and the fuzzy boundary contraction forces the dispersed samples or groups within the fuzzy boundary to gather tightly, jointly mitigating local cohesion and global sparseness while forming the ideal global structural distribution. Extensive experiments demonstrate that a graph auto-encoder with the fuzzy boundary significantly outperforms current state-of-the-art GCL models in both downstream tasks and quantitative analysis.
|
Graph contrastive learning, Classification, Clustering
| null | 1,768
| null |
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] | null |
2,737
|
CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features
|
https://openreview.net/forum?id=JZIJxr9KsO
|
[
"Xiaokun Feng",
"Dailing Zhang",
"Shiyu Hu",
"Xuchen Li",
"Meiqi Wu",
"Jing Zhang",
"Xiaotang Chen",
"Kaiqi Huang"
] |
Poster
|
Effectively modeling and utilizing spatiotemporal features from RGB and other modalities (e.g., depth, thermal, and event data, denoted as X) is the core of RGB-X tracker design.
Existing methods often employ two parallel branches to separately process the RGB and X input streams, requiring the model to simultaneously handle two dispersed feature spaces, which complicates both the model structure and computation process.
More critically, intra-modality spatial modeling within each dispersed space incurs substantial computational overhead, limiting resources for inter-modality spatial modeling and temporal modeling.
To address this, we propose a novel tracker, CSTrack, which focuses on modeling Compact Spatiotemporal features to achieve simple yet effective tracking.
Specifically, we first introduce an innovative Spatial Compact Module that integrates the RGB-X dual input streams into a compact spatial feature, enabling thorough intra- and inter-modality spatial modeling.
Additionally, we design an efficient Temporal Compact Module that compactly represents temporal features by constructing the refined target distribution heatmap.
Extensive experiments validate the effectiveness of our compact spatiotemporal modeling method, with CSTrack achieving new SOTA results on mainstream RGB-X benchmarks. The code and models will be released at: https://github.com/XiaokunFeng/CSTrack.
|
RGB-X Tracking, Multimodal Tracking, Compact Spatiotemporal Features
| null | 1,757
| null |
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https://github.com/XiaokunFeng/CSTrack
|
2,738
|
Noise-Guided Predicate Representation Extraction and Diffusion-Enhanced Discretization for Scene Graph Generation
|
https://openreview.net/forum?id=tkDtiOeLtx
|
[
"Guoqing Zhang",
"Shichao Kan",
"Fanghui Zhang",
"Wanru Xu",
"Yue Zhang",
"Yigang Cen"
] |
Poster
|
Scene Graph Generation (SGG) is a fundamental task in visual understanding, aimed at providing more precise local detail comprehension for downstream applications. Existing SGG methods often overlook the diversity of predicate representations and the consistency among similar predicates when dealing with long-tail distributions. As a result, the model's decision layer fails to effectively capture details from the tail end, leading to biased predictions. To address this, we propose a Noise-Guided Predicate Representation Extraction and Diffusion-Enhanced Discretization (NoDIS) method. On the one hand, expanding the predicate representation space enhances the model's ability to learn both common and rare predicates, thus reducing prediction bias caused by data scarcity. We propose a conditional diffusion model to reconstructs features and increase the diversity of representations for same category predicates. On the other hand, independent predicate representations in the decision phase increase the learning complexity of the decision layer, making accurate predictions more challenging. To address this issue, we introduce a discretization mapper that learns consistent representations among similar predicates, reducing the learning difficulty and decision ambiguity in the decision layer. To validate the effectiveness of our method, we integrate NoDIS with various SGG baseline models and conduct experiments on multiple datasets. The results consistently demonstrate superior performance.
|
Scene Graph Generation; Diffusion Model; Feature Reconstruction
|
For the scene graph generation task, we propose, for the first time, a feature reconstruction enhancement method based on diffusion models and discretization mapping to mitigate biased predictions caused by the long-tail distribution problem.
| 1,755
| null |
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] |
https://github.com/gavin-gqzhang/NoDIS
|
2,739
|
Ensemble Learned Bloom Filters: Two Oracles are Better than One
|
https://openreview.net/forum?id=ikZH6D9IGt
|
[
"Ming Lin",
"Lin CHEN"
] |
Poster
|
Bloom filters (BF) are space-efficient probabilistic data structures for approximate membership testing. Boosted by the proliferation of machine learning, learned Bloom filters (LBF) were recently proposed by augmenting the canonical BFs with a learned oracle as a pre-filter, the size of which is crucial to the compactness of the overall system. In this paper, inspired by ensemble learning, we depart from the state-of-the-art single-oracle LBF structure by demonstrating that, by leveraging multiple learning oracles of smaller size and carefully optimizing the accompanied backup filters, we can significantly boost the performance of LBF under the same space budget.
We then design and optimize ensemble learned Bloom filters for mutually independent and correlated learning oracles respectively. We also empirically demonstrate the performance improvement of our propositions under three practical data analysis tasks.
|
Learned Bloom Filters, membership testing, learning-augmented algorithms, combinatorial optimization
| null | 1,750
| null |
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] | null |
2,740
|
From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models
|
https://openreview.net/forum?id=DatAXrGzlc
|
[
"Mingjia Yin",
"Junwei Pan",
"Hao Wang",
"Ximei Wang",
"Shangyu Zhang",
"Jie Jiang",
"Defu Lian",
"Enhong Chen"
] |
Poster
|
Click-Through Rate (CTR) prediction models estimate the probability of users clicking on items based on feature interactions, inherently following a discriminative paradigm.
However, this paradigm is prone to embedding dimensional collapse and information redundancy due to limitations of vanilla feature embeddings.
This motivates us to reformulate it into a generative paradigm to generate new feature embeddings.
Unlike sequential recommendation, which naturally fits a generative "next-item prediction" paradigm, it's hard to formulate CTR models into this paradigm without explicit feature order.
Therefore, we propose a novel Supervised Feature Generation framework for CTR models, shifting from the discriminative "feature interaction" paradigm to the generative "feature generation" paradigm.
Specifically, we predict each feature embedding based on the concatenation of all feature embeddings.
Besides, this paradigm naturally accommodates a supervised binary cross-entropy loss to indicate whether the sample is positive or negative.
The framework can reformulate nearly every existing CTR model and bring significant performance lifts.
Moreover, it produces less-collapsed and redundancy-reduced feature embeddings, thereby mitigating the inherent limitations of the discriminative paradigm.
The code can be found at https://github.com/USTC-StarTeam/GE4Rec.
|
Generative model, Recommender system, Feature interaction
| null | 1,748
| null |
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] |
https://github.com/USTC-StarTeam/GE4Rec
|
2,741
|
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees
|
https://openreview.net/forum?id=h3KHwZCnxH
|
[
"Yannis Montreuil",
"Axel Carlier",
"Lai Xing Ng",
"Wei Tsang Ooi"
] |
Poster
|
Two-stage Learning-to-Defer (L2D) enables optimal task delegation by assigning each input to either a fixed main model or one of several offline experts, supporting reliable decision-making in complex, multi-agent environments. However, existing L2D frameworks assume clean inputs and are vulnerable to adversarial perturbations that can manipulate query allocation—causing costly misrouting or expert overload. We present the first comprehensive study of adversarial robustness in two-stage L2D systems. We introduce two novel attack strategies—untargeted and targeted—which respectively disrupt optimal allocations or force queries to specific agents. To defend against such threats, we propose SARD, a convex learning algorithm built on a family of surrogate losses that are provably Bayes-consistent and $(\mathcal{R}, \mathcal{G})$-consistent. These guarantees hold across classification, regression, and multi-task settings. Empirical results demonstrate that SARD significantly improves robustness under adversarial attacks while maintaining strong clean performance, marking a critical step toward secure and trustworthy L2D deployment.
|
Learning to defer, learning with abstention, robustness, learning theory
|
Adversarial robustness in the context of two-stage Learning-to-Defer: Introducing novel attacks and establishing defenses with guarantees
| 1,746
| null |
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] | null |
2,742
|
DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization
|
https://openreview.net/forum?id=WIIfdh0GJu
|
[
"Zhenglin Zhou",
"Xiaobo Xia",
"Fan Ma",
"Hehe Fan",
"Yi Yang",
"Tat-Seng Chua"
] |
Poster
|
Text-to-3D generation automates 3D content creation from textual descriptions, which offers transformative potential across various fields. However, existing methods often struggle to align generated content with human preferences, limiting their applicability and flexibility. To address these limitations, in this paper, we propose DreamDPO, an optimization-based framework that integrates human preferences into the 3D generation process, through direct preference optimization. Practically, DreamDPO first constructs pairwise examples, then validates their alignment with human preferences using reward or large multimodal models, and lastly optimizes the 3D representation with a preference-driven loss function. By leveraging relative preferences, DreamDPO reduces reliance on precise quality evaluations while enabling fine-grained controllability through preference-guided optimization. Experiments demonstrate that DreamDPO achieves state-of-the-art results, and provides higher-quality and more controllable 3D content compared to existing methods. The code and models will be open-sourced.
|
3D Generation, Human Preference, Direct Preference Optimization
|
DreamDPO is a framework that incorporates human preferences into text-to-3D generation, achieving state-of-the-art results with improved quality and fine-grained controllability.
| 1,732
|
2502.04370
|
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] |
https://github.com/ZhenglinZhou/DreamDPO
|
2,743
|
Nonconvex Theory of M-estimators with Decomposable Regularizers
|
https://openreview.net/forum?id=QJKTAcLf1H
|
[
"Weiwei Liu"
] |
Poster
|
High-dimensional inference addresses scenarios where the dimension of the data approaches, or even surpasses, the sample size. In these settings, the regularized $M$-estimator is a common technique for inferring parameters. (Negahban et al.,
2009) establish a unified framework for establishing convergence rates in the context of high-dimensional scaling, demonstrating that estimation errors are confined within a restricted set, and revealing fast convergence rates. The key assumption underlying their work is the convexity of the loss function. However, many loss functions in high-dimensional contexts are nonconvex. This leads to the question: if the loss function is nonconvex, do estimation errors still fall within a restricted set? If yes, can we recover convergence rates of the estimation error under nonconvex situations? This paper provides affirmative answers to these critical questions.
|
$M$-estimators, Nonconvex Theory
| null | 1,728
| null |
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] | null |
2,744
|
Weight matrices compression based on PDB model in deep neural networks
|
https://openreview.net/forum?id=2hbx3wG2I1
|
[
"Xiaoling Wu",
"Junpeng Zhu",
"Zeng Li"
] |
Poster
|
Weight matrix compression has been demonstrated to effectively reduce overfitting and improve the generalization performance of deep neural networks. Compression is primarily achieved by filtering out noisy eigenvalues of the weight matrix. In this work, a novel **Population Double Bulk (PDB) model** is proposed to characterize the eigenvalue behavior of the weight matrix, which is more general than the existing Population Unit Bulk (PUB) model. Based on PDB model and Random Matrix Theory (RMT), we have discovered a new **PDBLS algorithm** for determining the boundary between noisy eigenvalues and information. A **PDB Noise-Filtering algorithm** is further introduced to reduce the rank of the weight matrix for compression. Experiments show that our PDB model fits the empirical distribution of eigenvalues of the weight matrix better than the PUB model, and our compressed weight matrices have lower rank at the same level of test accuracy. In some cases, our compression method can even improve generalization performance when labels contain noise. The code is avaliable at https://github.com/xlwu571/PDBLS.
|
Weight Matrix Compression, Noise-filtering, Generalization Performance, Random Matrix Theory
|
Weight Matrix Compression with PDB Model
| 1,722
| null |
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] |
https://github.com/xlwu571/PDBLS
|
2,745
|
Learning Bayesian Nash Equilibrium in Auction Games via Approximate Best Response
|
https://openreview.net/forum?id=djAtMRB1Y4
|
[
"Kexin Huang",
"Ziqian Chen",
"Xue Wang",
"Chongming Gao",
"Jinyang Gao",
"Bolin Ding",
"Xiang Wang"
] |
Poster
|
Auction plays a crucial role in many modern trading environments, including online advertising and public resource allocation. As the number of competing bidders increases, learning Bayesian Nash Equilibrium (BNE) in auctions faces significant scalability challenges. Existing methods often experience slow convergence in large-scale auctions.
For example, in a classic symmetric auction setting, the convergence rate depends on the number of bidders quadratically.
To address this issue, we propose the *Approximate Best Response Gradient* method, a new approach for learning BNE efficiently in auction games. We leverage an analytic solution for gradient estimation to enable efficient gradient computation during optimization. Moreover, we introduce the *Best Response Distance* objective, which serves as an upper bound of approximation quality to BNE. By optimizing the new objective, our method is proven to achieve a local convergence rate independent of bidder numbers and circumvent the traditional quadratic complexity in the classic symmetric setting.
Extensive experiments across various auction formats demonstrate that our approach accelerates convergence and enhances learning efficiency in complex auction settings.
|
Auction, Equilibrium, Optimization
| null | 1,718
| null |
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] |
https://github.com/Hesse73/Approx-BR-Grad
|
2,746
|
Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration
|
https://openreview.net/forum?id=0WQJ6DFSKp
|
[
"Xinjie Yao",
"Yu Wang",
"Pengfei Zhu",
"Wanyu Lin",
"Ruipu Zhao",
"Zhoupeng Guo",
"Weihao Li",
"Qinghua Hu"
] |
Poster
|
Traditional machine societies rely on data-driven learning, overlooking interactions and limiting knowledge acquisition from model interplay. To address these issues, we revisit the development of machine societies by drawing inspiration from the evolutionary processes of human societies. Motivated by Social Learning (SL), this paper introduces a practical paradigm of Socialized Coevolution (SC). Compared to most existing methods focused on knowledge distillation and multi-task learning, our work addresses a more challenging problem: not only enhancing the capacity to solve new downstream tasks but also improving the performance of existing tasks through inter-model interactions. Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks. Specifically, we introduce the dynamic hierarchical collaboration and dynamic selective collaboration modules to enable dynamic and effective interactions among models, allowing them to acquire knowledge from these interactions. Finally, we explore potential future applications of combining SL and SC, discuss open questions, and propose directions for future research, aiming to spark interest in this emerging and exciting interdisciplinary field. Our code will be publicly available at https://github.com/yxjdarren/SC.
|
socialized coevolution, cross-task collaboration, dynamic weighting, knowledge interaction
|
We introduce a practical socialized coevolution paradigm with rigid mathematical motivation and explanation rooted in information theory.
| 1,715
| null |
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] | null |
2,747
|
Mechanisms of Projective Composition of Diffusion Models
|
https://openreview.net/forum?id=QV0PcBbfTd
|
[
"Arwen Bradley",
"Preetum Nakkiran",
"David Berthelot",
"James Thornton",
"Joshua M. Susskind"
] |
Poster
|
We study the theoretical foundations of composition in diffusion models, with a particular focus on out-of-distribution extrapolation and length-generalization. Prior work has shown that composing distributions via linear score combination can achieve promising results, including length-generalization in some cases (Du et al., 2023; Liu et al., 2022). However, our theoretical understanding of how and why such compositions work remains incomplete. In fact, it is not even entirely clear what it means for composition to "work". This paper starts to address these fundamental gaps. We begin by precisely defining one possible desired result of composition, which we call *projective composition*. Then, we investigate: (1) when linear score combinations provably achieve projective composition, (2) whether reverse-diffusion sampling can generate the desired composition, and (3) the conditions under which composition fails. We connect our theoretical analysis to prior empirical observations where composition has either worked or failed, for reasons that were unclear at the time. Finally, we propose a simple heuristic to help predict the success or failure of new compositions.
|
diffusion models, composition, theory
|
We theoretically study compositions of diffusion models.
| 1,670
|
2502.04549
|
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] | null |
2,748
|
Fast Video Generation with Sliding Tile Attention
|
https://openreview.net/forum?id=U74MOXPEJd
|
[
"Peiyuan Zhang",
"Yongqi Chen",
"Runlong Su",
"Hangliang Ding",
"Ion Stoica",
"Zhengzhong Liu",
"Hao Zhang"
] |
Poster
|
Diffusion Transformers (DiTs) with 3D full attention power state-of-the-art video generation, but suffer from prohibitive compute cost -- when generating just a 5-second 720P video, attention alone takes 800 out of 950 seconds of total inference time. This paper introduces sliding tile attention (STA) to address this challenge. STA leverages the
observation that attention scores in pretrained video diffusion models predominantly concentrate within localized 3D windows. By sliding and attending over local spatial-temporal region, STA eliminates redundancy from full attention. Unlike traditional token-wise sliding window attention (SWA), STA operates tile-by-tile with a novel hardware-aware sliding window design, preserving expressiveness while being \emph{hardware-efficient}. With careful kernel-level optimizations, STA offers the first efficient 2D/3D sliding-window-like attention implementation, achieving 58.79\% MFU -- 7.17× faster than prior art methods. On the leading video DiT model, Hunyuan, it accelerates attention by 1.6–10x over FlashAttention-3, yielding a 1.36–3.53× end-to-end speedup with no or minimum quality loss.
|
Diffusion model, attention, machine learning system.
| null | 1,666
|
2502.04507
|
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] |
https://github.com/hao-ai-lab/FastVideo
|
2,749
|
On the Learnability of Distribution Classes with Adaptive Adversaries
|
https://openreview.net/forum?id=h1O10vT4aW
|
[
"Tosca Lechner",
"Alex Bie",
"Gautam Kamath"
] |
Poster
|
We consider the question of learnability of distribution classes in the presence of adaptive adversaries -- that is, adversaries capable of intercepting the samples requested by a learner and applying manipulations with full knowledge of the samples before passing it on to the learner. This stands in contrast to oblivious adversaries, who can only modify the underlying distribution the samples come from but not their i.i.d.\ nature. We formulate a general notion of learnability with respect to adaptive adversaries, taking into account the budget of the adversary. We show that learnability with respect to additive adaptive adversaries is a strictly stronger condition than learnability with respect to additive oblivious adversaries.
|
distribution learning, robustness
| null | 1,664
| null |
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] | null |
2,750
|
Guided Zeroth-Order Methods for Stochastic Non-convex Problems with Decision-Dependent Distributions
|
https://openreview.net/forum?id=cRmuEY7jhb
|
[
"Yuya Hikima",
"Hiroshi Sawada",
"Akinori Fujino"
] |
Poster
|
In this study, we tackle an optimization problem with a known function and an unknown decision-dependent distribution, which arises in a variety of applications and is often referred to as a performative prediction problem.
To solve the problem, several zeroth-order methods have been developed because the gradient of the objective function cannot be obtained explicitly due to the unknown distribution.
Although these methods have theoretical convergence, they cannot utilize the information on the known function, which limits their efficiency in reducing the objective value.
To overcome this issue, we propose new zeroth-order methods that generate effective update directions by utilizing information on the known function.
As theoretical results, we show the convergence of our methods to stationary points and provide the worst-case sample complexity analysis, which improves the state of the arts when the maximum objective value dominates the cube root of the decision variable's dimensionality in order.
Our simulation experiments on multiple applications show that our methods output solutions with lower objective values than the existing zeroth-order methods do.
|
Zeroth-order method, performative prediction, decision-dependent distribution, guided evolutionary strategy
|
Improved zeroth-order methods for stochastic non-convex optimization problems with decision-dependent distributions
| 1,660
| null |
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] |
https://github.com/Yuya-Hikima/ICML2025-Guided-Zeroth-Order-Methods
|
2,751
|
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields
|
https://openreview.net/forum?id=vhjuemZuRU
|
[
"David Keetae Park",
"Xihaier Luo",
"Guang Zhao",
"Seungjun Lee",
"Miruna Oprescu",
"Shinjae Yoo"
] |
Poster
|
Spatiotemporal learning is challenging due to the intricate interplay between spatial and temporal dependencies, the high dimensionality of the data, and scalability constraints. These challenges are further amplified in scientific domains, where data is often irregularly distributed (e.g., missing values from sensor failures) and high-volume (e.g., high-fidelity simulations), posing additional computational and modeling difficulties. In this paper, we present SCENT, a novel framework for scalable and continuity-informed spatiotemporal representation learning. SCENT unifies interpolation, reconstruction, and forecasting within a single architecture. Built on a transformer-based encoder-processor-decoder backbone, SCENT introduces learnable queries to enhance generalization and a query-wise cross-attention mechanism to effectively capture multi-scale dependencies. To ensure scalability in both data size and model complexity, we incorporate a sparse attention mechanism, enabling flexible output representations and efficient evaluation at arbitrary resolutions. We validate SCENT through extensive simulations and real-world experiments, demonstrating state-of-the-art performance across multiple challenging tasks while achieving superior scalability.
|
Spatiotemporal Learning, Scientific Data, Neural Fields, Implicit Neural Representation
|
SCENT learns continuous spatiotemporal representations from sparse data, enabling joint reconstruction, interpolation, and forecasting.
| 1,656
|
2504.12262
|
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] | null |
2,752
|
REG: Rectified Gradient Guidance for Conditional Diffusion Models
|
https://openreview.net/forum?id=uNK4ftGdnq
|
[
"Zhengqi Gao",
"Kaiwen Zha",
"Tianyuan Zhang",
"Zihui Xue",
"Duane S Boning"
] |
Poster
|
Guidance techniques are simple yet effective for improving conditional generation in diffusion models. Albeit their empirical success, the practical implementation of guidance diverges significantly from its theoretical motivation. In this paper, we reconcile this discrepancy by replacing the scaled marginal distribution target, which we prove theoretically invalid, with a valid scaled joint distribution objective. Additionally, we show that the established guidance implementations are approximations to the intractable optimal solution under no future foresight constraint. Building on these theoretical insights, we propose rectified gradient guidance (REG), a versatile enhancement designed to boost the performance of existing guidance methods. Experiments on 1D and 2D demonstrate that REG provides a better approximation to the optimal solution than prior guidance techniques, validating the proposed theoretical framework. Extensive experiments on class-conditional ImageNet and text-to-image generation tasks show that incorporating REG consistently improves FID and Inception/CLIP scores across various settings compared to its absence.
|
Diffusion models, classifier-free guidance, conditional generation
|
Albeit their empirical success, the practical implementation of guidance diverges significantly from its theoretical motivation. Our work reconciles this discrepancy by building the correct guidance theory for conditional diffusion models.
| 1,655
|
2501.18865
|
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] |
https://github.com/zhengqigao/REG
|
2,753
|
Towards Learning to Complete Anything in Lidar
|
https://openreview.net/forum?id=vWPzKn6usZ
|
[
"Ayça Takmaz",
"Cristiano Saltori",
"Neehar Peri",
"Tim Meinhardt",
"Riccardo de Lutio",
"Laura Leal-Taixé",
"Aljosa Osep"
] |
Poster
|
We propose CAL (Complete Anything in Lidar) for Lidar-based shape-completion in-the-wild. This is closely related to Lidar-based semantic/panoptic scene completion. However, contemporary methods can only complete and recognize objects from a closed vocabulary labeled in existing Lidar datasets. Different to that, our zero-shot approach leverages the temporal context from multi-modal sensor sequences to mine object shapes and semantic features of observed objects. These are then distilled into a Lidar-only instance-level completion and recognition model. Although we only mine partial shape completions, we find that our distilled model learns to infer full object shapes from multiple such partial observations across the dataset. We show that our model can be prompted on standard benchmarks for Semantic and Panoptic Scene Completion, localize objects as (amodal) 3D bounding boxes, and recognize objects beyond fixed class vocabularies.
|
Zero-shot segmentation; Lidar scene completion; Lidar perception
|
Zero-Shot Lidar Panoptic Scene Completion
| 1,654
|
2504.12264
|
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] | null |
2,754
|
Adaptive Self-improvement LLM Agentic System for ML Library Development
|
https://openreview.net/forum?id=gdsZ3uMPsY
|
[
"Genghan Zhang",
"Weixin Liang",
"Olivia Hsu",
"Kunle Olukotun"
] |
Poster
|
ML libraries, often written in architecture-specific programming languages (ASPLs) that target domain-specific architectures, are key to efficient ML systems. However, writing these high-performance ML libraries is challenging because it requires expert knowledge of both ML algorithms and the ASPL. Large language models (LLMs), on the other hand, have shown general coding capabilities. However, challenges remain when using LLMs for generating ML libraries using ASPLs because 1) this task is complicated even for human experts and 2) there are limited code examples due to the esoteric and evolving nature of ASPLs. We present an adaptive self-improvement agentic system that enables LLMs to perform such complex reasoning under limited data by iteratively improving their capability through self-generated experience. In order to evaluate the effectiveness of our system, we construct a benchmark of a typical ML library and generate ASPL code with both open and closed-source LLMs on this benchmark. Our results show improvements of up to $3.9\times$ over a baseline single LLM.
|
LLM agents, Self-improvement learning, Machine learning library
|
How can we use LLMs to improve the efficiency of themselves? We introduce an LLM agentic system with self-improvement for ML library development using hardware architecture specific language, automatically implementing 25 of 26 key LLM operators.
| 1,652
|
2502.02534
|
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] |
https://github.com/zhang677/PCL-lite
|
2,755
|
Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion
|
https://openreview.net/forum?id=ZMrdvSm7xi
|
[
"Ruixiang ZHANG",
"Shuangfei Zhai",
"Yizhe Zhang",
"James Thornton",
"Zijing Ou",
"Joshua M. Susskind",
"Navdeep Jaitly"
] |
Poster
|
Discrete diffusion is a promising framework for modeling and generating discrete data.
In this work, we present Target Concrete Score Matching (TCSM), a novel and versatile objective for training and fine-tuning discrete diffusion models.
TCSM provides a general framework with broad applicability. It supports pre-training discrete diffusion models directly from data samples, and many existing discrete diffusion approaches naturally emerge as special cases of our more general TCSM framework.
Furthermore, the same TCSM objective extends to post-training of discrete diffusion models, including fine-tuning using reward functions or preference data, and distillation of knowledge from pre-trained autoregressive models.
These new capabilities stem from the core idea of TCSM, estimating the concrete score of the target distribution, which resides in the original (clean) data space. This allows seamless integration with reward functions and pre-trained models, which inherently only operate in the clean data space rather than the noisy intermediate spaces of diffusion processes.
Our experiments on language modeling tasks demonstrate that TCSM matches or surpasses current methods. Additionally, TCSM is versatile, applicable to both pre-training and post-training scenarios, offering greater flexibility and sample efficiency.
|
discrete diffusion, concrete score, discrete flow matching
|
We propose Target Concrete Score Matching as a holistic framework for discrete diffusion modeling.
| 1,646
|
2504.16431
|
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] | null |
2,756
|
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
|
https://openreview.net/forum?id=UoaxRN88oR
|
[
"Harrish Thasarathan",
"Julian Forsyth",
"Thomas Fel",
"Matthew Kowal",
"Konstantinos G. Derpanis"
] |
Poster
|
We present Universal Sparse Autoencoders (USAEs), a framework for uncovering and aligning interpretable concepts spanning multiple pretrained deep neural networks. Unlike existing concept-based interpretability methods, which focus on a single model, USAEs jointly learn a universal concept space that can reconstruct and interpret the internal activations of multiple models at once. Our core insight is to train a single, overcomplete sparse autoencoder (SAE) that ingests activations from any model and decodes them to approximate the activations of any other model under consideration. By optimizing a shared objective, the learned dictionary captures common factors of variation—concepts—across different tasks, architectures, and datasets. We show that USAEs discover semantically coherent and important universal concepts across vision models; ranging from low-level features (e.g., colors and textures) to higher-level structures (e.g., parts and objects). Overall, USAEs provide a powerful new method for interpretable cross-model analysis and offers novel applications—such as coordinated activation maximization—that open avenues for deeper insights in multi-model AI systems.
|
Interpretability, Concepts, Sparse Autoencoders
|
We introduce Universal Sparse Autoencoders, a new framework for discovering and aligning interpretable concepts shared across multiple deep neural networks
| 1,640
|
2502.03714
|
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] |
https://github.com/YorkUCVIL/UniversalSAE
|
2,757
|
Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing
|
https://openreview.net/forum?id=BKOeyZal0x
|
[
"Kento Nishi",
"Rahul Ramesh",
"Maya Okawa",
"Mikail Khona",
"Hidenori Tanaka",
"Ekdeep Singh Lubana"
] |
Poster
|
Knowledge Editing (KE) algorithms alter models' weights to perform targeted updates to incorrect, outdated, or otherwise unwanted factual associations. However, recent work has shown that applying KE can adversely affect models' broader factual recall accuracy and diminish their reasoning abilities. Although these studies give insights into the potential harms of KE algorithms, e.g., performance evaluations on benchmarks, little is understood about why such destructive failures occur. Motivated by this, we define a novel synthetic task in which a Transformer is trained from scratch to internalize a "structured" knowledge graph. The structure enforces relationships between entities of the graph, such that editing a factual association has "trickling effects" on other entities (e.g., altering X's parent is Y to Z affects who X's siblings' parent is). Through evaluations of edited models on this task, we show that KE inadvertently affects representations of entities beyond the targeted one, distorting relevant structures that allow a model to infer unseen knowledge about an entity. We call this phenomenon representation shattering and demonstrate that it degrades models' factual recall and reasoning performance. We further corroborate our findings in naturalistic settings with pre-trained Llama and Mamba models as well. Overall, our work yields a precise mechanistic hypothesis to explain why KE has adverse effects on model abilities.
|
mechanistic interpretability, knowledge editing, transformers
|
Our work introduces a synthetic framework which enables for a deeper understanding of Knowledge Editing and its side effects with regards to model representations.
| 1,639
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2410.17194
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] |
https://github.com/KentoNishi/KE-ICML-2025
|
2,758
|
Agent-as-a-Judge: Evaluate Agents with Agents
|
https://openreview.net/forum?id=Nn9POI9Ekt
|
[
"Mingchen Zhuge",
"Changsheng Zhao",
"Dylan R. Ashley",
"Wenyi Wang",
"Dmitrii Khizbullin",
"Yunyang Xiong",
"Zechun Liu",
"Ernie Chang",
"Raghuraman Krishnamoorthi",
"Yuandong Tian",
"Yangyang Shi",
"Vikas Chandra",
"Jürgen Schmidhuber"
] |
Poster
|
Contemporary evaluation techniques are inadequate for agentic systems. These approaches either focus exclusively on final outcomes---ignoring the step-by-step nature of the thinking done by agentic systems---or require excessive manual labour. To address this, we introduce the **Agent-as-a-Judge** framework, wherein agentic systems are used to evaluate agentic systems. This is a natural extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving processes for more precise evaluations. We apply the Agent-as-a-Judge framework to the task of code generation. To overcome issues with existing benchmarks and provide a proof-of-concept testbed for Agent-as-a-Judge, we present **DevAI**, a new benchmark of 55 realistic AI code generation tasks. DevAI includes rich manual annotations, like a total of 365 hierarchical solution requirements, which make it particularly suitable for an agentic evaluator. We benchmark three of the top code-generating agentic systems using Agent-as-a-Judge and find that our framework dramatically outperforms LLM-as-a-Judge and is as reliable as our human evaluation baseline. Altogether, we believe that this work represents a concrete step towards enabling vastly more sophisticated agentic systems. To help that, our dataset and the full implementation of Agent-as-a-Judge will be publically available at https://github.com/metauto-ai/agent-as-a-judge
|
Code Generation, Agent-as-a-Judge, AI Developer, AI Judge, LLM
|
This paper introduces the Agent-as-a-Judge, and we also collected a dataset helps verify our idea.
| 1,636
| null |
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] |
https://github.com/metauto-ai/agent-as-a-judge
|
2,759
|
Fraud-Proof Revenue Division on Subscription Platforms
|
https://openreview.net/forum?id=ibX0A4agKU
|
[
"Abheek Ghosh",
"Tzeh Yuan Neoh",
"Nicholas Teh",
"Giannis Tyrovolas"
] |
Poster
|
We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentivize manipulation. We formalize three types of manipulation-resistance axioms and examine which existing rules satisfy these. We show that a mechanism widely used by streaming platforms, not only fails to prevent fraud, but also makes detecting manipulation computationally intractable. We also introduce a novel rule, ScaledUserProp, that satisfies all three manipulation-resistance axioms. Finally, experiments with both real-world and synthetic streaming data support ScaledUserProp as a fairer alternative compared to existing rules.
|
fraud-proof, revenue division, subscription platforms, mechanism design
| null | 1,632
| null |
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] |
https://github.com/nicteh/Fraud-Proof-Revenue-Division
|
2,760
|
Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention
|
https://openreview.net/forum?id=MwBxNUl9AV
|
[
"Stephan Rabanser",
"Ali Shahin Shamsabadi",
"Olive Franzese",
"Xiao Wang",
"Adrian Weller",
"Nicolas Papernot"
] |
Poster
|
Cautious predictions—where a machine learning model abstains when uncertain—are crucial for limiting harmful errors in safety-critical applications. In this work, we identify a novel threat: a dishonest institution can exploit these mechanisms to discriminate or unjustly deny services under the guise of uncertainty. We demonstrate the practicality of this threat by introducing an uncertainty-inducing attack called Mirage, which deliberately reduces confidence in targeted input regions, thereby covertly disadvantaging specific individuals. At the same time, Mirage maintains high predictive performance across all data points. To counter this threat, we propose Confidential Guardian, a framework that analyzes calibration metrics on a reference dataset to detect artificially suppressed confidence. Additionally, it employs zero-knowledge proofs of verified inference to ensure that reported confidence scores genuinely originate from the deployed model. This prevents the provider from fabricating arbitrary model confidence values while protecting the model’s proprietary details. Our results confirm that Confidential Guardian effectively prevents the misuse of cautious predictions, providing verifiable assurances that abstention reflects genuine model uncertainty rather than malicious intent.
|
uncertainty, abstention, rejection, selective prediction, zero knowledge, confidence, auditing
|
We show that a model owner can artificially introduce uncertainty into their model and provide a corresponding detection mechanism.
| 1,628
|
2505.23968
|
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] |
https://github.com/cleverhans-lab/confidential-guardian
|
2,761
|
GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration
|
https://openreview.net/forum?id=QdELyl0FST
|
[
"Yuhang Li",
"Ruokai Yin",
"Donghyun Lee",
"Shiting Xiao",
"Priyadarshini Panda"
] |
Poster
|
We introduce GPTAQ, a novel finetuning-free quantization method for compressing large-scale transformer architectures.
Unlike the previous GPTQ method, which independently calibrates each layer, we always match the quantized layer's output to the exact output in the full-precision model, resulting in a scheme that we call *asymmetric calibration*. Such a scheme can effectively reduce the quantization error accumulated in previous layers. We analyze this problem using optimal brain compression to derive a close-formed solution. The new solution explicitly minimizes the quantization error as well as the accumulated asymmetry error. Furthermore, we utilize various techniques to parallelize the solution calculation, including channel parallelization, neuron decomposition, and Cholesky reformulation for matrix fusion. As a result, GPTAQ is easy to implement, simply using 20 more lines of code than GPTQ but improving its performance under low-bit quantization. Remarkably, on a single GPU, we quantize a 405B language transformer as well as EVA-02—the rank first vision transformer that achieves 90% pretraining Imagenet accuracy. Code is available at [Github](https://github.com/Intelligent-Computing-Lab-Yale/GPTAQ).
|
Transformers, Quantization, Discrete Optimization
| null | 1,623
|
2504.02692
|
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] |
https://github.com/Intelligent-Computing-Lab-Yale/GPTAQ
|
2,762
|
Robust Sparsification via Sensitivity
|
https://openreview.net/forum?id=qKHLzCELGP
|
[
"Chansophea Wathanak In",
"Yi Li",
"David Woodruff",
"Xuan Wu"
] |
Poster
|
Robustness to outliers is important in machine learning. Many classical problems, including subspace embedding, clustering, and low-rank approximation, lack scalable, outlier-resilient algorithms. This paper considers machine learning problems of the form $\min_{x\in \mathbb{R}^d} F(x)$, where $F(x)=\sum_{i=1}^n F_i(x)$, and their robust counterparts $\min_{x\in\mathbb{R}^d} F^{(m)}(x)$, where $F^{(m)}(x)$ denotes the sum of all but the $m$ largest $F_i(x)$ values.
We develop a general framework for constructing $\epsilon$-coresets for such robust problems, where an $\epsilon$-coreset is a weighted subset of functions $\{F_1(x),\dots,F_n(x)\}$ that provides a $(1+\epsilon)$-approximation to $F(x)$. Specifically, if the original problem $F$ has total sensitivity $T$ and admits a vanilla $\epsilon$-coreset of size $S$, our algorithm constructs an $\epsilon$-coreset of size $\tilde{O}(\frac{mT}{\epsilon})+S$ for the robust objective $F^{(m)}$. This coreset size can be shown to be near-tight for $\ell_2$ subspace embedding. Our coreset algorithm has scalable running time and leads to new or improved algorithms for the robust optimization problems. Empirical evaluations demonstrate that our coresets outperform uniform sampling on real-world data sets.
|
Coreset, Robust Machine Learning, Subspace Embedding, Clustering
|
We construct robust coreset for any optimization problem that admits bounded sensitivity and vanilla coreset.
| 1,617
| null |
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] | null |
2,763
|
TeDS: Joint Learning of Diachronic and Synchronic Perspectives in Quaternion Space for Temporal Knowledge Graph Completion
|
https://openreview.net/forum?id=rWUV8Pti9x
|
[
"Jiujiang Guo",
"Mankun Zhao",
"Wenbin Zhang",
"Tianyi Xu",
"Linying Xu",
"Yu Jian",
"Yu Mei",
"Yu Ruiguo"
] |
Poster
|
Existing research on temporal knowledge graph completion treats temporal information as supplementary, without simulating various features of facts from a temporal perspective. This work summarizes features of temporalized facts from both diachronic and synchronic perspectives: (1) Diachronicity. Facts often exhibit varying characteristics and trends across different temporal domains; (2) Synchronicity. In specific temporal contexts, various relations between entities influence each other, generating latent semantics. To track above issues, we design a quaternion-based model, TeDS, which divides timestamps into diachronic and synchronic timestamps to support dual temporal perception: (a) Two composite quaternions fusing time and relation information are generated by reorganizing synchronic timestamp and relation quaternions, and Hamilton operator achieves their interaction. (b) Each time point is sequentially mapped to an angle and converted to scalar component of a quaternion using trigonometric functions to build diachronic timestamps. We then rotate relation by using Hamilton operator between it and diachronic timestamp. In this way, TeDS achieves deep integration of relations and time while accommodating different perspectives. Empirically, TeDS significantly outperforms SOTA models on six benchmarks.
|
Knowledge graph; Knowledge graph completion
|
The paper simulates and summarizes dual perspectives of temporalized facts and designs distinct temporal perceptions based on quaternions to enhance TKGC.
| 1,610
| null |
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] | null |
2,764
|
DLP: Dynamic Layerwise Pruning in Large Language Models
|
https://openreview.net/forum?id=11id5ppGZ8
|
[
"Yuli Chen",
"Bo Cheng",
"Jiale Han",
"Yingying Zhang",
"Yingting Li",
"Shuhao Zhang"
] |
Poster
|
Pruning has recently been widely adopted to reduce the parameter scale and improve the inference efficiency of Large Language Models (LLMs). Mainstream pruning techniques often rely on uniform layerwise pruning strategies, which can lead to severe performance degradation at high sparsity levels. Recognizing the varying contributions of different layers in LLMs, recent studies have shifted their focus toward non-uniform layerwise pruning. However, these approaches often rely on pre-defined values, which can result in suboptimal performance. To overcome these limitations, we propose a novel method called Dynamic Layerwise Pruning (DLP). This approach adaptively determines the relative importance of each layer by integrating model weights with input activation information, assigning pruning rates accordingly. Experimental results show that DLP effectively preserves model performance at high sparsity levels across multiple LLMs. Specifically, at 70% sparsity, DLP reduces the perplexity of LLaMA2-7B by 7.79 and improves the average accuracy by 2.7% compared to state-of-the-art methods. Moreover, DLP is compatible with various existing LLM compression techniques and can be seamlessly integrated into Parameter-Efficient Fine-Tuning (PEFT). We release the code\footnote{The code is available at: \url{https://github.com/ironartisan/DLP}.} to facilitate future research.
|
Pruning, Large Language Models, Model Compression
|
The importance of different layers is automatically determined by calculating weight and activation information, ensuring the model's performance is maintained under high-sparsity pruning.
| 1,605
|
2505.23807
|
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] | null |
2,765
|
Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
|
https://openreview.net/forum?id=Tw81RElDpe
|
[
"Nurbek Tastan",
"Samuel Horváth",
"Karthik Nandakumar"
] |
Poster
|
Collaborative learning enables multiple participants to learn a single global model by exchanging focused updates instead of sharing data. One of the core challenges in collaborative learning is ensuring that participants are rewarded fairly for their contributions, which entails two key sub-problems: contribution assessment and reward allocation. This work focuses on fair reward allocation, where the participants are incentivized through model rewards - differentiated final models whose performance is commensurate with the contribution. In this work, we leverage the concept of slimmable neural networks to collaboratively learn a shared global model whose performance degrades gracefully with a reduction in model width. We also propose a post-training fair allocation algorithm that determines the model width for each participant based on their contributions. We theoretically study the convergence of our proposed approach and empirically validate it using extensive experiments on different datasets and architectures. We also extend our approach to enable training-time model reward allocation.
|
federated learning, fairness, collaborative fairness
| null | 1,600
|
2502.04850
|
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] |
https://github.com/tnurbek/aequa
|
2,766
|
QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
|
https://openreview.net/forum?id=eIm0PQVu55
|
[
"Fengbin Guan",
"Xin Li",
"Zihao Yu",
"Yiting Lu",
"Zhibo Chen"
] |
Poster
|
In this work, we take the first exploration of the recently popular foundation model, *i.e.,* State Space Model/Mamba, in image quality assessment (IQA), aiming at observing and excavating the perception potential in vision Mamba. A series of works on Mamba has shown its significant potential in various fields, *e.g.,* segmentation and classification. However, the perception capability of Mamba remains under-explored. Consequently, we propose QMamba by revisiting and adapting the Mamba model for three crucial IQA tasks, *i.e.,* task-specific, universal, and transferable IQA, which reveals its clear advantages over existing foundational models, *e.g.,* Swin Transformer, ViT, and CNNs, in terms of perception and computational cost. To improve the transferability of QMamba, we propose the StylePrompt tuning paradigm, where lightweight mean and variance prompts are injected to assist task-adaptive transfer learning of pre-trained QMamba for different downstream IQA tasks. Compared with existing prompt tuning strategies, our StylePrompt enables better perceptual transfer with lower computational cost. Extensive experiments on multiple synthetic, authentic IQA datasets, and cross IQA datasets demonstrate the effectiveness of our proposed QMamba.
|
Image Quality Assessment, State Space Model, Prompt Tuning
| null | 1,594
|
2406.09546
|
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] | null |
2,767
|
Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
|
https://openreview.net/forum?id=W7phL2sNif
|
[
"Hanglei Hu",
"Yingying Guo",
"Zhikang Chen",
"Sen Cui",
"Fei Wu",
"Kun Kuang",
"Min Zhang",
"Bo Jiang"
] |
Poster
|
Personalized learning, especially data-based methods, has garnered widespread attention in recent years, aiming to meet individual student needs. However, many works rely on the implicit assumption that benchmarks are high-quality and well-annotated, which limits their practical applicability. In real-world scenarios, these benchmarks often exhibit long-tail distributions, significantly impacting model performance. To address this challenge, we propose a novel method called **N**eural-**C**ollapse-**A**dvanced personalized **L**earning (NCAL), designed to learn features that conform to the same simplex equiangular tight frame (ETF) structure. NCAL introduces Text-modality Collapse (TC) regularization to optimize the distribution of text embeddings within the large language model (LLM) representation space. Notably, NCAL is model-agnostic, making it compatible with various architectures and approaches, thereby ensuring broad applicability. Extensive experiments demonstrate that NCAL effectively enhances existing works, achieving new state-of-the-art performance. Additionally, NCAL mitigates class imbalance, significantly improving the model’s generalization ability.
|
Neural Collapse, Personalized Learning, Large Language Model
| null | 1,591
| null |
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] | null |
2,768
|
Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation
|
https://openreview.net/forum?id=Qwy6JIA8Os
|
[
"Yaowenhu",
"Wenxuan Tu",
"Yue Liu",
"Xinhang Wan",
"Junyi Yan",
"Taichun Zhou",
"Xinwang Liu"
] |
Poster
|
Deep graph clustering (DGC), which aims to unsupervisedly separate the nodes in an attribute graph into different clusters, has seen substantial potential in various industrial scenarios like community detection and recommendation. However, the real-world attribute graphs, e.g., social networks interactions, are usually large-scale and attribute-missing. To solve these two problems, we propose a novel DGC method termed **C**omplementary **M**ulti-**V**iew **N**eighborhood **D**ifferentiation ($\textit{CMV-ND}$), which preprocesses graph structural information into multiple views in a complete but non-redundant manner. First, to ensure completeness of the structural information, we propose a recursive neighborhood search that recursively explores the local structure of the graph by completely expanding node neighborhoods across different hop distances. Second, to eliminate the redundancy between neighborhoods at different hops, we introduce a neighborhood differential strategy that ensures no overlapping nodes between the differential hop representations. Then, we construct $K+1$ complementary views from the $K$ differential hop representations and the features of the target node. Last, we apply existing multi-view clustering or DGC methods to the views. Experimental results on six widely used graph datasets demonstrate that CMV-ND significantly improves the performance of various methods.
|
Deep Graph Clustering，Multi-view clustering，Graph Neighborhood Difference，Information Redundancy
|
We address large-scale deep graph clustering under missing attributes and bridge the gap between Multi-View Clustering and Deep Graph Clustering.
| 1,584
| null |
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] | null |
2,769
|
TtBA: Two-third Bridge Approach for Decision-Based Adversarial Attack
|
https://openreview.net/forum?id=wEtNrpA8QH
|
[
"Feiyang Wang",
"Xingquan Zuo",
"Hai Huang",
"Gang Chen"
] |
Poster
|
A key challenge in black-box adversarial attacks is the high query complexity in hard-label settings, where only the top-1 predicted label from the target deep model is accessible. In this paper, we propose a novel normal-vector-based method called Two-third Bridge Attack (TtBA). A innovative bridge direction is introduced which is a weighted combination of the current unit perturbation direction and its unit normal vector, controlled by a weight parameter $k$. We further use binary search to identify $k=k_\text{bridge}$, which has identical decision boundary as the current direction. Notably, we observe that $k=2/3 k_\text{bridge}$ yields a near-optimal perturbation direction, ensuring the stealthiness of the attack. In addition, we investigate the critical importance of local optima during the perturbation direction optimization process and propose a simple and effective approach to detect and escape such local optima. Experimental results on MNIST, FASHION-MNIST, CIFAR10, CIFAR100, and ImageNet datasets demonstrate the strong performance and scalability of our approach. Compared to state-of-the-art non-targeted and targeted attack methods, TtBA consistently delivers superior performance across most experimented datasets and deep learning models. Code is available at https://anonymous.4open.science/r/TtBA-6ECF.
|
Adversarial Attacks, Black Box Adversarial Attacks, Hard Label Attacks, Decision-Based Attacks, Machine Learning Security, Artificial Intelligence Security, Robustness
|
We propose a novel Two-third Bridge Attack (TtBA) for decision-based black-box attack.
| 1,583
| null |
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] |
https://github.com/BUPTAIOC/TtBA
|
2,770
|
CSV-Occ: Fusing Multi-frame Alignment for Occupancy Prediction with Temporal Cross State Space Model and Central Voting Mechanism
|
https://openreview.net/forum?id=7Dt3nH5TZq
|
[
"Ziming Zhu",
"Yu Zhu",
"Jiahao Chen",
"LingXiaofeng",
"Huanlei Chen",
"Lihua Sun"
] |
Poster
|
Recently, image-based 3D semantic occupancy prediction has become a hot topic in 3D scene understanding for autonomous driving.
Compared with the bounding box form of 3D object detection, the ability to describe the fine-grained contours of any obstacles in the scene is the key insight of voxel occupancy representation, which facilitates subsequent tasks of autonomous driving. In this work, we propose CSV-Occ to address the following two challenges: (1) Existing methods fuse temporal information based on the attention mechanism, but are limited by high complexity. We extend the state space model to support multi-input sequence interaction and conduct temporal modeling in a cascaded architecture, thereby reducing the computational complexity from quadratic to linear. (2) Existing methods are limited by semantic ambiguity, resulting in the centers of foreground objects often being predicted as empty voxels. We enable the model to explicitly vote for the instance center to which the voxels belong and spontaneously learn to utilize the other voxel features of the same instance to update the semantics of the internal vacancies of the objects from coarse to fine. Experiments on the Occ3D-nuScenes dataset show that our method achieves state-of-the-art in camera-based 3D semantic occupancy prediction and also performs well on lidar point cloud semantic segmentation on the nuScenes dataset. Therefore, we believe that CSV-Occ is beneficial to the community and industry of autonomous vehicles.
|
3D Semantic Occupancy Prediction, Semantic Segmentation, Autonomous Driving, Temporal Information, State Space Model, Voting Mechanism
| null | 1,568
| null |
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] | null |
2,771
|
Impossible Videos
|
https://openreview.net/forum?id=MNSW6U5zUA
|
[
"Zechen Bai",
"Hai Ci",
"Mike Zheng Shou"
] |
Poster
|
Synthetic videos nowadays is widely used to complement data scarcity and diversity of real-world videos.
Current synthetic datasets primarily replicate real-world scenarios, leaving impossible, counterfactual and anti-reality video concepts underexplored. This work aims to answer two questions: 1) Can today's video generation models effectively follow prompts to create impossible video content? 2) Are today's video understanding models good enough for understanding impossible videos?
To this end, we introduce *IPV-Bench*, a novel benchmark designed to evaluate and foster progress in video understanding and generation. *IPV-Bench* is underpinned by a comprehensive taxonomy, encompassing 4 domains, 14 categories.
It features diverse scenes that defy physical, biological, geographical, or social laws. Based on the taxonomy, a prompt suite is constructed to evaluate video generation models, challenging their prompt following and creativity capabilities. In addition, a video benchmark is curated to assess Video-LLMs on their ability of understanding impossible videos, which particularly requires reasoning on temporal dynamics and world knowledge. Comprehensive evaluations reveal limitations and insights for future directions of video models, paving the way for next-generation video models.
|
Videos, Benchmark, Evaluation, Impossible Videos, Counterfactual, Anti-reality
|
This work present a comprehensive benchmark of impossible videos and text prompts, aiming to evaluate sota video generation and understanding models.
| 1,560
|
2503.14378
|
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] | null |
2,772
|
Hgformer: Hyperbolic Graph Transformer for Collaborative Filtering
|
https://openreview.net/forum?id=lNTHwNF8IH
|
[
"Xin Yang",
"Xingrun Li",
"Heng Chang",
"Yang jinze",
"Xihong Yang",
"Shengyu Tao",
"Maiko Shigeno",
"Ningkang Chang",
"Junfeng Wang",
"Dawei Yin",
"Erxue Min"
] |
Poster
|
Recommender systems are increasingly spreading to different areas like e-commerce or video streaming to alleviate information overload. One of the most fundamental methods for recommendation is Collaborative Filtering (CF), which leverages historical user-item interactions to infer user preferences. In recent years, Graph Neural Networks (GNNs) have been extensively studied to capture graph structures in CF tasks. Despite this remarkable progress, local structure modeling and embedding distortion still remain two notable limitations in the majority of GNN-based CF methods. Therefore, in this paper, we propose a novel Hyperbolic Graph Transformer architecture, to tackle the long-tail problems in CF tasks. Specifically, the proposed framework is comprised of two essential modules: 1) Local Hyperbolic Graph Convolutional Network (LHGCN), which performs graph convolution entirely in the hyperbolic manifold and captures the local structure of each node; 2) Hyperbolic Transformer, which is comprised of hyperbolic cross-attention mechanisms to capture global information. Furthermore, to enable its feasibility on large-scale data, we introduce an unbiased approximation of the cross-attention for linear computational complexity, with a theoretical guarantee in approximation errors. Empirical experiments demonstrate that our proposed model outperforms the leading collaborative filtering methods and significantly mitigates the long-tail issue in CF tasks. Our implementations are available in \url{https://github.com/EnkiXin/Hgformer}.
|
Collaborative Filtering, Hyperbolic Representation Learning, Graph Transformer
| null | 1,559
| null |
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] |
https://github.com/EnkiXin/Hgformer
|
2,773
|
Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers
|
https://openreview.net/forum?id=sq5eL4jfsn
|
[
"Filip Szatkowski",
"Yaoyue Zheng",
"Fei Yang",
"Tomasz Trzcinski",
"Bartłomiej Twardowski",
"Joost van de Weijer"
] |
Poster
|
Continual learning is crucial for applying machine learning in challenging, dynamic, and often resource-constrained environments. However, catastrophic forgetting — overwriting previously learned knowledge when new information is acquired — remains a major challenge. In this work, we examine the intermediate representations in neural network layers during continual learning and find that such representations are less prone to forgetting, highlighting their potential to accelerate computation. Motivated by these findings, we propose to use auxiliary classifiers~(ACs) to enhance performance and demonstrate that integrating ACs into various continual learning methods consistently improves accuracy across diverse evaluation settings, yielding an average 10\% relative gain. We also leverage the ACs to reduce the average cost of the inference by 10-60\% without compromising accuracy, enabling the model to return the predictions before computing all the layers. Our approach provides a scalable and efficient solution for continual learning.
|
Continual Learning, Class-incremental Learning, Early-exits
|
We show how intermediate representations in neural networks can be leveraged to improve both performance and efficiency in continual learning through auxiliary classifiers (ACs)
| 1,554
|
2403.07404
|
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] |
https://github.com/fszatkowski/cl-auxiliary-classifiers
|
2,774
|
Mixture of Experts Made Intrinsically Interpretable
|
https://openreview.net/forum?id=6QERrXMLP2
|
[
"Xingyi Yang",
"Constantin Venhoff",
"Ashkan Khakzar",
"Christian Schroeder de Witt",
"Puneet K. Dokania",
"Adel Bibi",
"Philip Torr"
] |
Poster
|
Neurons in large language models often exhibit \emph{polysemanticity}, simultaneously encoding multiple unrelated concepts and obscuring interpretability.
Instead of relying on post-hoc methods, we present \textbf{MoE-X}, a mixture-of-experts (MoE) language model designed to be \emph{intrinsically} interpretable. Our approach is motivated by the observation that, in language models, wider networks with sparse activations are more likely to capture interpretable factors. however, directly training such large sparse networks is computationally prohibitive. MoE architectures offer a scalable alternative by activating only a subset of experts for any given input, inherently aligning with interpretability objectives. In MoE-X, we establish this connection by rewriting the MoE layer as an equivalent sparse, large MLP. This approach enables efficient scaling of the hidden size while maintaining sparsity. To further enhance interpretability, we enforce sparse activation within each expert and redesign the routing mechanism to prioritize experts with the highest activation sparsity. These designs ensure that only the most salient features are routed and processed by the experts. We evaluate MoE-X on chess and natural language tasks, showing that it achieves performance comparable to dense models while significantly improving interpretability. MoE-X achieves a perplexity better than GPT-2, with interpretability surpassing even sparse autoencoder (SAE)-based approaches.
|
Mixture of Expert; Interpretability; Polysemanticity
|
We present **MoE-X**, a mixture-of-experts (MoE) language model designed to be *intrinsically* interpretable.
| 1,553
|
2503.07639
|
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] | null |
2,775
|
Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
|
https://openreview.net/forum?id=EkoFXfSauv
|
[
"Hongyao Tang",
"Johan Obando-Ceron",
"Pablo Samuel Castro",
"Aaron Courville",
"Glen Berseth"
] |
Poster
|
Plasticity, or the ability of an agent to adapt to new tasks, environments, or distributions, is crucial for continual learning. In this paper, we study the loss of plasticity in deep continual RL from the lens of churn: network output variability induced by the data in each training batch. We demonstrate that (1) the loss of plasticity is accompanied by the exacerbation of churn due to the gradual rank decrease of the Neural Tangent Kernel (NTK) matrix; (2) reducing churn helps prevent rank collapse and adjusts the step size of regular RL gradients adaptively. Moreover, we introduce Continual Churn Approximated Reduction (C-CHAIN) and demonstrate it improves learning performance and outperforms baselines in a diverse range of continual learning environments on OpenAI Gym Control, ProcGen, DeepMind Control Suite, and MinAtar benchmarks.
|
Plasticity, Continual Learning, Reinforcement Learning, Generalization
|
Reducing churn prevents the rank decrease of NTK, thus mitigates plasticity loss and improves continual RL.
| 1,552
|
2506.00592
|
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] |
https://github.com/bluecontra/C-CHAIN
|
2,776
|
Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
|
https://openreview.net/forum?id=b6VYI1Bvo2
|
[
"Keyue Qiu",
"Yuxuan Song",
"Zhehuan Fan",
"Peidong Liu",
"Zhe Zhang",
"Mingyue Zheng",
"Hao Zhou",
"Wei-Ying Ma"
] |
Poster
|
Structure-Based Drug Design (SBDD) is crucial for identifying bioactive molecules. Recent deep generative models are faced with challenges in geometric structure modeling. A major bottleneck lies in the twisted probability path of multi-modalities—continuous 3D positions and discrete 2D topologies—which jointly determine molecular geometries. By establishing the fact that noise schedules decide the Variational Lower Bound (VLB) for the twisted probability path, we propose VLB-Optimal Scheduling (VOS) strategy in this under-explored area, which optimizes VLB as a path integral for SBDD. Our model effectively enhances molecular geometries and interaction modeling, achieving state-of-the-art PoseBusters passing rate of 95.9\% on CrossDock, more than 10\% improvement upon strong baselines, while maintaining high affinities and robust intramolecular validity evaluated on held-out test set. Code is available at https://github.com/AlgoMole/MolCRAFT.
|
Structure-Based Drug Design, Bayesian Flow Network, Noise Schedule
|
Twisted probability path induced by noise schedule with the optimal VLB
| 1,546
|
2505.07286
|
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] |
https://github.com/AlgoMole/MolCRAFT
|
2,777
|
TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation
|
https://openreview.net/forum?id=m5EIuQxt0x
|
[
"Tong Wu",
"Junzhe Shen",
"Zixia Jia",
"Yuxuan Wang",
"Zilong Zheng"
] |
Poster
|
Generating ultra-long sequences with large language models (LLMs) has become increasingly crucial but remains a highly time-intensive task, particularly for sequences up to 100K tokens. While traditional speculative decoding methods exist, simply extending their generation limits fails to accelerate the process and can be detrimental. Through an in-depth analysis, we identify three major challenges hindering efficient generation: frequent model reloading, dynamic key-value (KV) management and repetitive generation. To address these issues, we introduce TokenSwift, a novel framework designed to substantially accelerate the generation process of ultra-long sequences while maintaining the target model’s inherent quality. Experimental results demonstrate that TokenSwift achieves over $3 \times$ speedup across models of varying scales (1.5B, 7B, 8B, 14B) and architectures (MHA, GQA). This acceleration translates to hours of time savings for ultra-long sequence generation, establishing TokenSwift as a scalable and effective solution at unprecedented lengths.
|
Large Language Models, Lossless Acceleration, Ultra-Long Sequence Generation
| null | 1,543
|
2502.18890
|
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] |
https://github.com/bigai-nlco/TokenSwift
|
2,778
|
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
|
https://openreview.net/forum?id=YWLWUTtVF3
|
[
"Federico Errica",
"Henrik Christiansen",
"Viktor Zaverkin",
"Takashi Maruyama",
"Mathias Niepert",
"Francesco Alesiani"
] |
Poster
|
Long-range interactions are essential for the correct description of complex systems in many scientific fields. The price to pay for including them in the calculations, however, is a dramatic increase in the overall computational costs. Recently, deep graph networks have been employed as efficient, data-driven models for predicting properties of complex systems represented as graphs. These models rely on a message passing strategy that should, in principle, capture long-range information without explicitly modeling the corresponding interactions. In practice, most deep graph networks cannot really model long-range dependencies due to the intrinsic limitations of (synchronous) message passing, namely oversmoothing, oversquashing, and underreaching. This work proposes a general framework that \textit{learns to mitigate} these limitations: within a variational inference framework, we endow message passing architectures with the ability to adapt their depth and filter messages along the way. With theoretical and empirical arguments, we show that this strategy better captures long-range interactions, by competing with the state of the art on five node and graph prediction datasets.
|
Graph Machine Learning, Variational Inference, Oversmoothing, Oversquashing, Underreaching, Depth Learning
|
We propose a message-passing framework for graph learning to adapt the number layers during training and filter outgoing messages, in order to control oversmoothing, oversquashing, and underreaching.
| 1,540
|
2312.16560
|
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] |
https://github.com/nec-research/Adaptive-Message-Passing
|
2,779
|
Certified Unlearning for Neural Networks
|
https://openreview.net/forum?id=3rWQlV3s1I
|
[
"Anastasia Koloskova",
"Youssef Allouah",
"Animesh Jha",
"Rachid Guerraoui",
"Sanmi Koyejo"
] |
Poster
|
We address the problem of machine unlearning, where the goal is to remove the influence of specific training data from a model upon request, motivated by privacy concerns and regulatory requirements such as the “right to be forgotten.” Unfortunately, existing methods rely on restrictive assumptions or lack formal guarantees.
To this end, we propose a novel method for certified machine unlearning, leveraging the connection between unlearning and privacy amplification by stochastic post-processing. Our method uses noisy fine-tuning on the retain data, i.e., data that does not need to be removed, to ensure provable unlearning guarantees. This approach requires no assumptions about the underlying loss function, making it broadly applicable across diverse settings. We analyze the theoretical trade-offs in efficiency and accuracy and demonstrate empirically that our method not only achieves formal unlearning guarantees but also performs effectively in practice, outperforming existing baselines.
|
machine unlearning, privacy amplification by stochastic post processing, privacy amplification by iteration
| null | 1,539
|
2506.06985
|
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] |
https://github.com/stair-lab/certified-unlearning-neural-networks-icml-2025
|
2,780
|
A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
|
https://openreview.net/forum?id=p2smPMRQae
|
[
"Ibrahim Alabdulmohsin",
"Andreas Peter Steiner"
] |
Poster
|
Language exhibits a fractal structure in its information-theoretic complexity (i.e. bits per token), with self-similarity across scales and long-range dependence (LRD). In this work, we investigate whether large language models (LLMs) can replicate such fractal characteristics and identify conditions-such as temperature setting and prompting method-under which they may fail. Moreover, we find that the fractal parameters observed in natural language are contained within a narrow range, whereas those of LLMs' output vary widely, suggesting that fractal parameters might prove helpful in detecting a non-trivial portion of LLM-generated texts. Notably, these findings, and many others reported in this work, are robust to the choice of the architecture; e.g. Gemini 1.0 Pro, Mistral-7B and Gemma-2B. We also release a dataset comprising over 240,000 articles generated by various LLMs (both pretrained and instruction-tuned) with different decoding temperatures and prompting methods, along with their corresponding human-generated texts. We hope that this work highlights the complex interplay between fractal properties, prompting, and statistical mimicry in LLMs, offering insights for generating, evaluating and detecting synthetic texts.
|
large language models, fractals, Hurst exponent, Holder exponent, self-similarity, detection, decoding, instruction-tuning, prompting
|
We study the conditions for when LLMs can successfully replicate the fractal structure of language and relate this to the quality of output. We also release a dataset.
| 1,535
|
2502.14924
|
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] | null |
2,781
|
LineFlow: A Framework to Learn Active Control of Production Lines
|
https://openreview.net/forum?id=7tWpRX4Dxk
|
[
"Kai Müller",
"Martin Wenzel",
"Tobias Windisch"
] |
Poster
|
Many production lines require active control mechanisms, such as adaptive routing, worker reallocation, and rescheduling, to maintain optimal performance. However, designing these control systems is challenging for various reasons, and while reinforcement learning (RL) has shown promise in addressing these challenges, a standardized and general framework is still lacking. In this work, we introduce LineFlow, an extensible, open-source Python framework for simulating production lines of arbitrary complexity and training RL agents to control them. To demonstrate the capabilities and to validate the underlying theoretical assumptions of LineFlow, we formulate core subproblems of active line control in ways that facilitate mathematical analysis. For each problem, we provide optimal solutions for comparison. We benchmark state-of-the-art RL algorithms and show that the learned policies approach optimal performance in well-understood scenarios. However, for more complex, industrial-scale production lines, RL still faces significant challenges, highlighting the need for further research in areas such as reward shaping, curriculum learning, and hierarchical control.
|
Machine Learning for Manufacturing, Active production line control, Reinforcement Learning, Production line simulation
|
This paper presents LineFlow, an open-source extensible Python framework for simulating production lines and optimizing them using reinforcement learning.
| 1,525
|
2505.06744
|
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] |
https://github.com/hs-kempten/lineflow
|
2,782
|
Variance as a Catalyst: Efficient and Transferable Semantic Erasure Adversarial Attack for Customized Diffusion Models
|
https://openreview.net/forum?id=Fh9XlJnXb5
|
[
"Jiachen Yang",
"Yusong Wang",
"Yanmei Fang",
"Yunshu Dai",
"Fangjun Huang"
] |
Poster
|
Latent Diffusion Models (LDMs) enable fine-tuning with only a few images and have become widely used on the Internet. However, it can also be misused to generate fake images, leading to privacy violations and social risks. Existing adversarial attack methods primarily introduce noise distortions to generated images but fail to completely erase identity semantics.
In this work, we identify the variance of VAE latent code as a key factor that influences image distortion. Specifically, larger variances result in stronger distortions and ultimately erase semantic information. Based on this finding, we propose a Laplace-based (LA) loss function that optimizes along the fastest variance growth direction, ensuring each optimization step is locally optimal. Additionally, we analyze the limitations of existing methods and reveal that their loss functions often fail to align gradient signs with the direction of variance growth. They also struggle to ensure efficient optimization under different variance distributions. To address these issues, we further propose a novel Lagrange Entropy-based (LE) loss function.
Experimental results demonstrate that our methods achieve state-of-the-art performance on CelebA-HQ and VGGFace2. Both proposed loss functions effectively lead diffusion models to generate pure-noise images with identity semantics completely erased. Furthermore, our methods exhibit strong transferability across diverse models and efficiently complete attacks with minimal computational resources. Our work provides a practical and efficient solution for privacy protection.
|
Customized Latent Diffusion Models, Adversarial Attacks, Privacy Protection, Transferability, AI Security
| null | 1,524
| null |
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] |
https://github.com/youyuanyi/variance-as-Catalyst
|
2,783
|
SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
|
https://openreview.net/forum?id=OYbZWmNHwn
|
[
"Yuqi Fan",
"Zhiyong Cui",
"Zhenning Li",
"Yilong Ren",
"Haiyang Yu"
] |
Poster
|
Reliable planning is crucial for achieving autonomous driving. Rule-based planners are efficient but lack generalization, while learning-based planners excel in generalization yet have limitations in real-time performance and interpretability. In long-tail scenarios, these challenges make planning particularly difficult. To leverage the strengths of both rule-based and learning-based planners, we proposed the **Scenario-Aware Hybrid Planner** (SAH-Drive) for closed-loop vehicle trajectory planning. Inspired by human driving behavior, SAH-Drive combines a lightweight rule-based planner and a comprehensive learning-based planner, utilizing a dual-timescale decision neuron to determine the final trajectory. To enhance the computational efficiency and robustness of the hybrid planner, we also employed a diffusion proposal number regulator and a trajectory fusion module. The experimental results show that the proposed method significantly improves the generalization capability of the planning system, achieving state-of-the-art performance in interPlan, while maintaining computational efficiency without incurring substantial additional runtime.
|
diffusion model, autonomous driving, hybrid planner, trajectory planning
|
SAH-Drive combines a lightweight rule-based planner and an extensive learning-based planner, utilizing a dual-timescale decision neuron to determine the final trajectory.
| 1,523
| null |
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] |
https://github.com/richie-live/SAH-Drive
|
2,784
|
Gradient Boosting Reinforcement Learning
|
https://openreview.net/forum?id=URmGywyt59
|
[
"Benjamin Fuhrer",
"Chen Tessler",
"Gal Dalal"
] |
Poster
|
We present Gradient Boosting Reinforcement Learning (GBRL), a framework that adapts the strengths of Gradient Boosting Trees (GBT) to reinforcement learning (RL) tasks. While neural networks (NNs) have become the de facto choice for RL, they face significant challenges with structured and categorical features and tend to generalize poorly to out-of-distribution samples. These are challenges for which GBTs have traditionally excelled in supervised learning. However, GBT's application in RL has been limited. The design of traditional GBT libraries is optimized for static datasets with fixed labels, making them incompatible with RL's dynamic nature, where both state distributions and reward signals evolve during training. GBRL overcomes this limitation by continuously interleaving tree construction with environment interaction. Through extensive experiments, we demonstrate that GBRL outperforms NNs in domains with structured observations and categorical features, while maintaining competitive performance on standard continuous control benchmarks. Like its supervised learning counterpart, GBRL demonstrates superior robustness to out-of-distribution samples and better handles irregular state-action relationships.
|
Gradient Boosting Trees, Reinforcement Learning, Machine Learning
| null | 1,505
|
2407.08250
|
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] |
https://github.com/NVlabs/gbrl;https://github.com/NVlabs/gbrl_sb3
|
2,785
|
S2FGL: Spatial Spectral Federated Graph Learning
|
https://openreview.net/forum?id=pFQ3MnyIT6
|
[
"Zihan Tan",
"Suyuan Huang",
"Guancheng Wan",
"Wenke Huang",
"He Li",
"Mang Ye"
] |
Poster
|
Federated Graph Learning (FGL) combines the privacy-preserving capabilities of federated learning (FL) with the strong graph modeling capability of Graph Neural Networks (GNNs). Current research addresses subgraph-FL only from the structural perspective, neglecting the propagation of graph signals on spatial and spectral domains of the structure. From a spatial perspective, subgraph-FL introduces edge disconnections between clients, leading to disruptions in label signals and a degradation in the class knowledge of the global GNN. From a spectral perspective, spectral heterogeneity causes inconsistencies in signal frequencies across subgraphs, which makes local GNNs overfit the local signal propagation schemes. As a result, spectral client drifts occur, undermining global generalizability. To tackle the challenges, we propose a global knowledge repository to mitigate label signal disruption and a frequency alignment to address spectral client drifts.
The combination of $\textbf{S}$patial and $\textbf{S}$pectral strategies forms our framework $S^2$FGL. Extensive experiments on multiple datasets demonstrate the superiority of $S^2$FGL. The code is available at https://github.com/Wonder7racer/S2FGL.git.
|
Federated Learning, Safety in Machine Learning
|
We propose $S^2$FGL, a federated graph learning framework that mitigates label signal disruption and spectral client drifts to improve global model generalizability.
| 1,500
|
2507.02409
|
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https://github.com/Wonder7racer/S2FGL.git.
|
2,786
|
FG-CLIP: Fine-Grained Visual and Textual Alignment
|
https://openreview.net/forum?id=eih5Gy3Pjt
|
[
"Chunyu Xie",
"Bin Wang",
"Fanjing Kong",
"Jincheng Li",
"Dawei Liang",
"Gengshen Zhang",
"Dawei Leng",
"Yuhui Yin"
] |
Poster
|
Contrastive Language-Image Pre-training (CLIP) excels in multimodal tasks such as image-text retrieval and zero-shot classification but struggles with fine-grained understanding due to its focus on coarse-grained short captions. To address this, we propose Fine-Grained CLIP (FG-CLIP), which enhances fine-grained understanding through three key innovations. First, we leverage large multimodal models to generate 1.6 billion long caption-image pairs for capturing global-level semantic details. Second, a high-quality dataset is constructed with 12 million images and 40 million region-specific bounding boxes aligned with detailed captions to ensure precise, context-rich representations. Third, 10 million hard fine-grained negative samples are incorporated to improve the model's ability to distinguish subtle semantic differences. We construct a comprehensive dataset, termed FineHARD, by integrating high-quality region-specific annotations with challenging fine-grained negative samples. Corresponding training methods are meticulously designed for these data. Extensive experiments demonstrate that FG-CLIP outperforms the original CLIP and other state-of-the-art methods across various downstream tasks, including fine-grained understanding, open-vocabulary object detection, image-text retrieval, and general multimodal benchmarks. These results highlight FG-CLIP's effectiveness in capturing fine-grained image details and improving overall model performance. The data, code, and models are available at https://github.com/360CVGroup/FG-CLIP.
|
Vision-Language Model, Contrastive Learning, Image-Text Dataset
| null | 1,499
| null |
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https://github.com/360CVGroup/FG-CLIP
|
2,787
|
Rhomboid Tiling for Geometric Graph Deep Learning
|
https://openreview.net/forum?id=lTZEAqL1R1
|
[
"Yipeng Zhang",
"Longlong Li",
"Kelin Xia"
] |
Poster
|
Graph Neural Networks (GNNs) have proven effective for learning from graph-structured data through their neighborhood-based message passing framework. Many hierarchical graph clustering pooling methods modify this framework by introducing clustering-based strategies, enabling the construction of more expressive and powerful models. However, all of these message passing framework heavily rely on the connectivity structure of graphs, limiting their ability to capture the rich geometric features inherent in geometric graphs. To address this, we propose Rhomboid Tiling (RT) clustering, a novel clustering method based on the rhomboid tiling structure, which performs clustering by leveraging the complex geometric information of the data and effectively extracts its higher-order geometric structures. Moreover, we design RTPool, a hierarchical graph clustering pooling model based on RT clustering for graph classification tasks. The proposed model demonstrates superior performance, outperforming 21 state-of-the-art competitors on all the 7 benchmark datasets.
|
Rhomboid tiling, geometric deep learning, molecule property classification, graph pooling
| null | 1,494
|
2505.09586
|
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] | null |
2,788
|
Learning Fused State Representations for Control from Multi-View Observations
|
https://openreview.net/forum?id=cKnzZrIJBR
|
[
"Zeyu Wang",
"Yao-Hui Li",
"Xin Li",
"Hongyu Zang",
"Romain Laroche",
"Riashat Islam"
] |
Poster
|
Multi-View Reinforcement Learning (MVRL) seeks to provide agents with multi-view observations, enabling them to perceive environment with greater effectiveness and precision. Recent advancements in MVRL focus on extracting latent representations from multiview observations and leveraging them in control tasks. However, it is not straightforward to learn compact and task-relevant representations, particularly in the presence of redundancy, distracting information, or missing views. In this paper, we propose **M**ulti-view **F**usion **S**tate for **C**ontrol (**MFSC**), firstly incorporating bisimulation metric learning into MVRL to learn task-relevant representations. Furthermore, we propose a multiview-based mask and latent reconstruction auxiliary task that exploits shared information across views and improves MFSC’s robustness in missing views by introducing a mask token. Extensive experimental results demonstrate that our method outperforms existing approaches in MVRL tasks. Even in more realistic scenarios with interference or missing views, MFSC consistently maintains high performance. The project code is available at [https://github.com/zpwdev/MFSC](https://github.com/zpwdev/MFSC).
|
reinforcement learning, multi-view learning
|
We proposed a method to learn fused state representations for multi-view RL.
| 1,488
|
2502.01316
|
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] |
https://github.com/zpwdev/MFSC
|
2,789
|
Safety Reasoning with Guidelines
|
https://openreview.net/forum?id=BHwWLeXDYF
|
[
"Haoyu Wang",
"Zeyu Qin",
"Li Shen",
"Xueqian Wang",
"Dacheng Tao",
"Minhao Cheng"
] |
Poster
|
Training safe LLMs remains a critical challenge. The most widely used method, Refusal Training (RT), struggles to generalize against various Out-of-Distribution (OOD) jailbreaking attacks. Although various advanced methods have been proposed to address this issue, we instead question whether OOD attacks inherently surpass the capability of vanilla RT. Evaluations using Best-of-N (BoN) reveal significant safety improvements as N increases, indicating models possess adequate latent safety knowledge but RT fails to consistently elicit it under OOD scenarios. Further domain adaptation analysis reveals that direct RT causes reliance on superficial shortcuts, resulting in non-generalizable representation mappings. Inspired by our findings, we propose training model to perform safety reasoning for each query. Specifically, we synthesize reasoning supervision aligned with specified guidelines that reflect diverse perspectives on safety knowledge. This encourages model to engage in deeper reasoning, explicitly eliciting and utilizing latent safety knowledge for each query. Extensive experiments show that our method significantly improves model generalization against OOD attacks.
|
Safety Alignment, Safety Reasoning, Safety Generalization, OOD Generalization
| null | 1,486
|
2502.04040
|
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] | null |
2,790
|
Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding
|
https://openreview.net/forum?id=0ObGn4e1IS
|
[
"Jinze Li",
"Yixing Xu",
"Haiduo Huang",
"Xuanwu Yin",
"Dong Li",
"Edith C. H. Ngai",
"Emad Barsoum"
] |
Poster
|
Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens, enabling efficient multi-token generation. However, existing methods assume that all tokens within a sequence are equally important, employing identical head structures and relying on a single-generation paradigm, either serial or parallel. To this end, we theoretically demonstrate that initial tokens in the draft sequence are more important than later ones. Building on this insight, we propose Gumiho, a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. For later tokens, we utilize multiple lightweight MLP heads operating in parallel to enhance efficiency. By allocating more advanced model structures and longer running times to the early heads, Gumiho achieves improved overall performance. The experimental results demonstrate that our method outperforms existing approaches, fully validating its effectiveness. Our code is available at https://github.com/AMD-AIG-AIMA/Gumiho.
|
Speculative Decoding, Large Language Models
|
Gumiho speeds up LLM text generation with a hybrid approach: it prioritizes accuracy for crucial early predicted tokens and uses faster parallel processing for later ones. This design leads to improved overall inference speed.
| 1,485
|
2503.10135
|
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] |
https://github.com/AMD-AIG-AIMA/Gumiho
|
2,791
|
ConText: Driving In-context Learning for Text Removal and Segmentation
|
https://openreview.net/forum?id=UuvcoHGivw
|
[
"Fei Zhang",
"Pei Zhang",
"Baosong Yang",
"Fei Huang",
"Yanfeng Wang",
"Ya Zhang"
] |
Poster
|
This paper presents the first study on adapting the visual in-context learning (V-ICL) paradigm to optical character recognition tasks, specifically focusing on text removal and segmentation. Most existing V-ICL generalists employ a reasoning-as-reconstruction approach: they turn to using a straightforward image-label compositor as the prompt and query input, and then masking the query label to generate the desired output. This direct prompt confines the model to a challenging single-step reasoning process. To address this, we propose a task-chaining compositor in the form of image-removal-segmentation, providing an enhanced prompt that elicits reasoning with enriched intermediates. Additionally, we introduce context-aware aggregation, integrating the chained prompt pattern into the latent query representation, thereby strengthening the model's in-context reasoning. We also consider the issue of visual heterogeneity, which complicates the selection of homogeneous demonstrations in text recognition. Accordingly, this is effectively addressed through a simple self-prompting strategy, preventing the model's in-context learnability from devolving into specialist-like, context-free inference. Collectively, these insights culminate in our ConText model, which achieves new state-of-the-art across both in- and out-of-domain benchmarks. The code is available at https://github.com/Ferenas/ConText.
|
In-context learning, text removal, text segmentation
|
This paper for the first time explores the visual in-context learning for some OCR tasks.
| 1,480
|
2506.03799
|
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] |
https://github.com/Ferenas/ConText
|
2,792
|
Towards Rationale-Answer Alignment of LVLMs via Self-Rationale Calibration
|
https://openreview.net/forum?id=G7fZ0GTUb6
|
[
"Yuanchen Wu",
"Ke Yan",
"Shouhong Ding",
"Ziyin Zhou",
"Xiaoqiang Li"
] |
Poster
|
Large Vision-Language Models (LVLMs) have manifested strong visual question answering capability. However, they still struggle with aligning the rationale and the generated answer, leading to inconsistent reasoning and incorrect responses. To this end, this paper introduces Self-Rationale Calibration (SRC) framework to iteratively calibrate the alignment between rationales and answers. SRC begins by employing a lightweight “rationale fine-tuning” approach, which modifies the model’s response format to require a rationale before deriving answer without explicit prompts. Next, SRC searches a diverse set of candidate responses from the fine-tuned LVLMs for each sample, followed by a proposed pairwise scoring strategy using a tailored scoring model, R-Scorer, to evaluate both rationale quality and factual consistency of candidates. Based on a confidence-weighted preference curation process, SRC decouples the alignment calibration into a preference fine-tuning manner, leading to significant improvements of LVLMs in perception, reasoning, and generalization across multiple benchmarks. Our results emphasize the rationale-oriented alignment in exploring the potential of LVLMs.
|
LVLM; Post-training;
| null | 1,477
| null |
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] | null |
2,793
|
Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts
|
https://openreview.net/forum?id=9W3CSuHoFA
|
[
"Yike Yuan",
"Ziyu Wang",
"Zihao Huang",
"Defa Zhu",
"Xun Zhou",
"Jingyi Yu",
"Qiyang Min"
] |
Poster
|
Diffusion models have emerged as mainstream framework in visual generation. Building upon this success, the integration of Mixture of Experts (MoE) methods has shown promise in enhancing model scalability and performance. In this paper, we introduce Race-DiT, a novel MoE model for diffusion transformers with a flexible routing strategy, Expert Race. By allowing tokens and experts to compete together and select the top candidates, the model learns to dynamically assign experts to critical tokens. Additionally, we propose per-layer regularization to address challenges in shallow layer learning, and router similarity loss to prevent mode collapse, ensuring better expert utilization. Extensive experiments on ImageNet validate the effectiveness of our approach, showcasing significant performance gains while promising scaling properties.
|
Mixture of Experts, Diffusion Models
|
MoE with flexible routing strategy designed for diffusion models
| 1,476
|
2503.16057
|
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] | null |
2,794
|
MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization
|
https://openreview.net/forum?id=e6vlZdLlIK
|
[
"Kangyu Zhu",
"Peng Xia",
"Yun Li",
"Hongtu Zhu",
"Sheng Wang",
"Huaxiu Yao"
] |
Poster
|
The advancement of Large Vision-Language Models (LVLMs) has propelled their application in the medical field. However, Medical LVLMs (Med-LVLMs) encounter factuality challenges due to modality misalignment, where the models prioritize textual knowledge over visual input, leading to hallucinations that contradict information in medical images. Previous attempts to enhance modality alignment in Med-LVLMs through preference optimization have inadequately addressed clinical relevance in preference data, making these samples easily distinguishable and reducing alignment effectiveness. In response, we propose MMedPO, a novel multimodal medical preference optimization approach that considers the clinical relevance of preference samples to enhance Med-LVLM alignment. MMedPO curates multimodal preference data by introducing two types of dispreference: (1) plausible hallucinations injected through target Med-LVLMs or GPT-4o to produce medically inaccurate responses, and (2) lesion region neglect achieved through local lesion-noising, disrupting visual understanding of critical areas. We then calculate clinical relevance for each sample based on scores from multiple Med-LLMs and visual tools, enabling effective alignment. Our experiments demonstrate that MMedPO significantly enhances factual accuracy in Med-LVLMs, achieving substantial improvements over existing preference optimization methods by 14.2% and 51.7% on the Med-VQA and report generation tasks, respectively. Our code are available in https://github.com/aiming-lab/MMedPO}{https://github.com/aiming-lab/MMedPO.
|
medical vision-language models, preference optimization
|
A clinical-aware multimodal preference optimization method for vision-language model
| 1,468
|
2412.06141
|
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] |
https://github.com/aiming-lab/MMedPO}{https://github.com/aiming-lab/MMedPO
|
2,795
|
HPS: Hard Preference Sampling for Human Preference Alignment
|
https://openreview.net/forum?id=hLvWwRZkok
|
[
"Xiandong Zou",
"Wanyu Lin",
"Yuchen Li",
"Pan Zhou"
] |
Poster
|
Aligning Large Language Model (LLM) responses with human preferences is vital for building safe and controllable AI systems. While preference optimization methods based on Plackett-Luce (PL) and Bradley-Terry (BT) models have shown promise, they face challenges such as poor handling of harmful content, inefficient use of dispreferred responses, and, specifically for PL, high computational costs. To address these issues, we propose Hard Preference Sampling (HPS), a novel framework for robust and efficient human preference alignment. HPS introduces a training loss that prioritizes the most preferred response while rejecting all dispreferred and harmful ones. It emphasizes “hard” dispreferred responses — those closely resembling preferred ones — to enhance the model’s rejection capabilities. By leveraging a single-sample Monte Carlo sampling strategy, HPS reduces computational overhead while maintaining alignment quality. Theoretically, HPS improves sample efficiency over existing PL methods and maximizes the reward margin between preferred and dispreferred responses, ensuring clearer distinctions. Experiments on HH-RLHF and PKU-Safety datasets validate HPS’s effectiveness, achieving comparable BLEU and reward scores while greatly improving reward margins and thus reducing harmful content generation.
|
Alignment, Preference Optimization, RLHF, Large Language Models
|
We propose Hard Preference Sampling (HPS), a novel framework for robust and efficient human preference alignment.
| 1,462
|
2502.14400
|
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] |
https://github.com/LVLab-SMU/HPS
|
2,796
|
Semi-Supervised Blind Quality Assessment with Confidence-quantifiable Pseudo-label Learning for Authentic Images
|
https://openreview.net/forum?id=038rEwbChh
|
[
"Yan Zhong",
"Chenxi Yang",
"Suyuan Zhao",
"Tingting Jiang"
] |
Poster
|
This paper presents CPL-IQA, a novel semi-supervised blind image quality assessment (BIQA) framework for authentic distortion scenarios. To address the challenge of limited labeled data in IQA area, our approach leverages confidence-quantifiable pseudo-label learning to effectively utilize unlabeled authentically distorted images. The framework operates through a preprocessing stage and two training phases: first converting MOS labels to vector labels via entropy minimization, followed by an iterative process that alternates between model training and label optimization. The key innovations of CPL-IQA include a manifold assumption-based label optimization strategy and a confidence learning method for pseudo-labels, which enhance reliability and mitigate outlier effects. Experimental results demonstrate the framework's superior performance on real-world distorted image datasets, offering a more standardized semi-supervised learning paradigm without requiring additional supervision or network complexity.
|
BIQA, Semi-supervised Learning, Pseudo-labels, Confidence Learning, Real-world Distorted Images
|
This paper introduces a novel semi-supervised framework for blind image quality assessment based on label propagation, utilizing confidence-quantifiable pseudo-labels to enhance performance.
| 1,461
| null |
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] | null |
2,797
|
RUN: Reversible Unfolding Network for Concealed Object Segmentation
|
https://openreview.net/forum?id=tiYNbKwL7T
|
[
"Chunming He",
"Rihan Zhang",
"Fengyang Xiao",
"Chengyu Fang",
"Longxiang Tang",
"Yulun Zhang",
"Linghe Kong",
"Deng-Ping Fan",
"Kai Li",
"Sina Farsiu"
] |
Poster
|
Concealed object segmentation (COS) is a challenging problem that focuses on identifying objects that are visually blended into their background. Existing methods often employ reversible strategies to concentrate on uncertain regions but only focus on the mask level, overlooking the valuable of the RGB domain. To address this, we propose a Reversible Unfolding Network (RUN) in this paper. RUN formulates the COS task as a foreground-background separation process and incorporates an extra residual sparsity constraint to minimize segmentation uncertainties. The optimization solution of the proposed model is unfolded into a multistage network, allowing the original fixed parameters to become learnable. Each stage of RUN consists of two reversible modules: the Segmentation-Oriented Foreground Separation (SOFS) module and the Reconstruction-Oriented Background Extraction (ROBE) module. SOFS applies the reversible strategy at the mask level and introduces Reversible State Space to capture non-local information. ROBE extends this to the RGB domain, employing a reconstruction network to address conflicting foreground and background regions identified as distortion-prone areas, which arise from their separate estimation by independent modules. As the stages progress, RUN gradually facilitates reversible modeling of foreground and background in both the mask and RGB domains, reducing false-positive and false-negative regions. Extensive experiments demonstrate the superior performance of RUN and underscore the promise of unfolding-based frameworks for COS and other high-level vision tasks. Code is available at https://github.com/ChunmingHe/RUN.
|
Concealed object segmentation, Deep unfolding network, Reversible modeling strategy, Camouflaged object detection
|
The first unfolding-based concealed object segmentation algorithms.
| 1,459
|
2501.18783
|
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] |
https://github.com/ChunmingHe/RUN
|
2,798
|
Test-time Adaptation on Graphs via Adaptive Subgraph-based Selection and Regularized Prototypes
|
https://openreview.net/forum?id=lC40m2jjUO
|
[
"Yusheng Zhao",
"Qixin Zhang",
"Xiao Luo",
"Junyu Luo",
"Wei Ju",
"Zhiping Xiao",
"Ming Zhang"
] |
Poster
|
Test-time adaptation aims to adapt a well-trained model using test data only, without accessing training data. It is a crucial topic in machine learning, enabling a wide range of applications in the real world, especially when it comes to data privacy. While existing works on test-time adaptation primarily focus on Euclidean data, research on non-Euclidean graph data remains scarce. Prevalent graph neural network methods could encounter serious performance degradation in the face of test-time domain shifts. In this work, we propose a novel method named Adaptive Subgraph-based Selection and Regularized Prototype Supervision (ASSESS) for reliable test-time adaptation on graphs. Specifically, to achieve flexible selection of reliable test graphs, ASSESS adopts an adaptive selection strategy based on fine-grained individual-level subgraph mutual information. Moreover, to utilize the information from both training and test graphs, ASSESS constructs semantic prototypes from the well-trained model as prior knowledge from the unknown training graphs and optimizes the posterior given the unlabeled test graphs. We also provide a theoretical analysis of the proposed algorithm. Extensive experiments verify the effectiveness of ASSESS against various baselines.
|
test-time adaptation, graph neural networks
|
This paper explores a novel yet under-explored problem of test-time adaptation on graphs and proposes a novel method named Adaptive Subgraph-based Selection and Regularized Prototype Supervision (ASSESS) to solve the problem.
| 1,457
| null |
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] | null |
2,799
|
Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling
|
https://openreview.net/forum?id=gbeZKej40m
|
[
"Hongzhi Huang",
"Defa Zhu",
"Banggu Wu",
"Yutao Zeng",
"Ya Wang",
"Qiyang Min",
"zhou Xun"
] |
Poster
|
Tokenization is a fundamental component of large language models (LLMs), yet its influence on model scaling and performance is not fully explored. In this paper, we introduce Over-Tokenized Transformers, a novel framework that decouples input and output vocabularies to improve language modeling performance. Specifically, our approach scales up input vocabularies to leverage multi-gram tokens. Through extensive experiments, we uncover a log-linear relationship between input vocabulary size and training loss, demonstrating that larger input vocabularies consistently enhance model performance, regardless of model size. Using a large input vocabulary, we achieve performance comparable to double-sized baselines with no additional cost. Our findings highlight the importance of tokenization in scaling laws and provide practical insight for tokenizer design, paving the way for more efficient and powerful LLMs.
|
LLM, Tokenization, Pre-training, Scaling-laws
|
The input and output vocabulary is decoupled, where the input vocabularies is scaled up large, achieving performance of double-sized models without extra cost.
| 1,456
|
2501.16975
|
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