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3,200
|
CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities
|
https://openreview.net/forum?id=3pk0p4NGmQ
|
[
"Yuxuan Zhu",
"Antony Kellermann",
"Dylan Bowman",
"Philip Li",
"Akul Gupta",
"Adarsh Danda",
"Richard Fang",
"Conner Jensen",
"Eric Ihli",
"Jason Benn",
"Jet Geronimo",
"Avi Dhir",
"Sudhit Rao",
"Kaicheng Yu",
"Twm Stone",
"Daniel Kang"
] |
Spotlight
|
Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture-the-Flag competitions or lack comprehensive coverage. Building a benchmark for real-world vulnerabilities involves both specialized exper-
tise to reproduce exploits and a systematic approach to evaluating unpredictable attacks. To address this challenge, we introduce CVE-Bench, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. In CVE-Bench, we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our experiments show that the state-of-the-art agent framework can exploit up to 13% of the vulnerabilities.
|
benchmark, cybersecurity, llm, agent
|
We introduce a cybersecurity benchmark for evaluating the capability of AI agents in exploiting real-world vulnerabilities of web applications.
| 5,058
| null |
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] |
https://github.com/uiuc-kang-lab/cve-bench
|
3,201
|
LOCATE 3D: Real-World Object Localization via Self-Supervised Learning in 3D
|
https://openreview.net/forum?id=FKi6yjXwCN
|
[
"Paul McVay",
"Sergio Arnaud",
"Ada Martin",
"Arjun Majumdar",
"Krishna Murthy Jatavallabhula",
"Phillip Thomas",
"Ruslan Partsey",
"Daniel Dugas",
"Abha Gejji",
"Alexander Sax",
"Vincent-Pierre Berges",
"Mikael Henaff",
"Ayush Jain",
"Ang Cao",
"Ishita Prasad",
"Mrinal Kalakrishnan",
"Michael Rabbat",
"Nicolas Ballas",
"Mido Assran",
"Oleksandr Maksymets",
"et al. (2 additional authors not shown)"
] |
Spotlight
|
We present LOCATE 3D, a model for localizing objects in 3D scenes from referring expressions like "the small coffee table between the sofa and the lamp." LOCATE 3D sets a new state-of-the-art on standard referential grounding benchmarks and showcases robust generalization capabilities. Notably, LOCATE 3D operates directly on sensor observation streams (posed RGB-D frames), enabling real-world deployment on robots and AR devices. Key to our approach is 3D-JEPA, a novel self-supervised learning (SSL) algorithm applicable to sensor point clouds. It takes as input a 3D pointcloud featurized using 2D foundation models (CLIP, DINO). Subsequently, masked prediction in latent space is employed as a pretext task to aid the self-supervised learning of contextualized pointcloud features. Once trained, the 3D-JEPA encoder is finetuned alongside a language-conditioned decoder to jointly predict 3D masks and bounding boxes. Additionally, we introduce LOCATE 3D DATASET, a new dataset for 3D referential grounding, spanning multiple capture setups with over 130K annotations. This enables a systematic study of generalization capabilities as well as a stronger model. Code, models and dataset can be found at the project website: locate3d.atmeta.com
|
self-supervised learning, object localization, referring expressions, 3D language grounding
|
A model that can localize objects in 3D from textual referring expressions.
| 5,047
|
2504.14151
|
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] |
https://github.com/facebookresearch/locate-3d
|
3,202
|
Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator
|
https://openreview.net/forum?id=OJ6WE7F8tK
|
[
"Kaiwen Zheng",
"Yongxin Chen",
"Huayu Chen",
"Guande He",
"Ming-Yu Liu",
"Jun Zhu",
"Qinsheng Zhang"
] |
Spotlight
|
While likelihood-based generative models, particularly diffusion and autoregressive models, have achieved remarkable fidelity in visual generation, the maximum likelihood estimation (MLE) objective, which minimizes the forward KL divergence, inherently suffers from a mode-covering tendency that limits the generation quality under limited model capacity. In this work, we propose Direct Discriminative Optimization (DDO) as a unified framework that integrates likelihood-based generative training and GAN-type discrimination to bypass this fundamental constraint by exploiting reverse KL and self-generated negative signals. Our key insight is to parameterize a discriminator implicitly using the likelihood ratio between a learnable target model and a fixed reference model, drawing parallels with the philosophy of Direct Preference Optimization (DPO). Unlike GANs, this parameterization eliminates the need for joint training of generator and discriminator networks, allowing for direct, efficient, and effective finetuning of a well-trained model to its full potential beyond the limits of MLE. DDO can be performed iteratively in a self-play manner for progressive model refinement, with each round requiring less than 1\% of pretraining epochs. Our experiments demonstrate the effectiveness of DDO by significantly advancing the previous SOTA diffusion model EDM, reducing FID scores from 1.79/1.58/1.96 to new records of 1.30/0.97/1.26 on CIFAR-10/ImageNet-64/ImageNet 512$\times$512 datasets without any guidance mechanisms, and by consistently improving both guidance-free and CFG-enhanced FIDs of visual autoregressive models on ImageNet 256$\times$256.
|
Diffusion Models, Visual Autoregressive Models, GAN, Generation Quality
|
an efficient and effective finetuning method for enhancing diffusion models and visual autoregressive models
| 5,007
|
2503.01103
|
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] |
https://github.com/NVlabs/DDO
|
3,203
|
Geometric Representation Condition Improves Equivariant Molecule Generation
|
https://openreview.net/forum?id=79O2XccGXZ
|
[
"Zian Li",
"Cai Zhou",
"Xiyuan Wang",
"Xingang Peng",
"Muhan Zhang"
] |
Spotlight
|
Recent advances in molecular generative models have demonstrated great promise for accelerating scientific discovery, particularly in drug design. However, these models often struggle to generate high-quality molecules, especially in conditional scenarios where specific molecular properties must be satisfied. In this work, we introduce GeoRCG, a general framework to improve molecular generative models by integrating geometric representation conditions with provable theoretical guarantees. We decompose the generation process into two stages: first, generating an informative geometric representation; second, generating a molecule conditioned on the representation. Compared with single-stage generation, the easy-to-generate representation in the first stage guides the second stage generation toward a high-quality molecule in a goal-oriented way. Leveraging EDM and SemlaFlow as base generators, we observe significant quality improvements in unconditional molecule generation on the widely used QM9 and GEOM-DRUG datasets. More notably, in the challenging conditional molecular generation task, our framework achieves an average 50\% performance improvement over state-of-the-art approaches, highlighting the superiority of conditioning on semantically rich geometric representations. Furthermore, with such representation guidance, the number of diffusion steps can be reduced to as small as 100 while largely preserving the generation quality achieved with 1,000 steps, thereby significantly reducing the generation iterations needed.
|
molecule generation, equivariant generative models, representation, geometric deep learning, diffusion models
|
We propose a two-stage, model-agnostic generative approach that effectively leverages molecule representations to improve the generation quality of molecule generative models.
| 4,856
|
2410.03655
|
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] | null |
3,204
|
Graph Adaptive Autoregressive Moving Average Models
|
https://openreview.net/forum?id=UFlyLkvyAE
|
[
"Moshe Eliasof",
"Alessio Gravina",
"Andrea Ceni",
"Claudio Gallicchio",
"Davide Bacciu",
"Carola-Bibiane Schönlieb"
] |
Spotlight
|
Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods either compromise on permutation equivariance or limit their focus to pairwise interactions rather than sequences. Building on the connection between Autoregressive Moving Average (ARMA) and SSM, in this paper, we introduce GRAMA, a Graph Adaptive method based on a learnable ARMA framework that addresses these limitations.
By transforming from static to sequential graph data, GRAMA leverages the strengths of the ARMA framework, while preserving permutation equivariance. Moreover, GRAMA incorporates a selective attention mechanism for dynamic learning of ARMA coefficients, enabling efficient and flexible long-range information propagation. We also establish theoretical connections between GRAMA and Selective SSMs, providing insights into its ability to capture long-range dependencies. Experiments on 26 synthetic and real-world datasets demonstrate that GRAMA consistently outperforms backbone models and performs competitively with state-of-the-art methods.
|
Graph Neural Networks, Auto-regressive Moving Average
|
We introduce GRAMA, an ARMA-based framework that preserves permutation equivariance, and adapts coefficients via selective attention for long-range propagation. Experimental results on 22 datasets demonstrate its effectiveness.
| 4,819
|
2501.12732
|
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] | null |
3,205
|
am-ELO: A Stable Framework for Arena-based LLM Evaluation
|
https://openreview.net/forum?id=EUH4VUCXay
|
[
"Zirui Liu",
"Jiatong Li",
"Yan Zhuang",
"Qi Liu",
"Shuanghong Shen",
"Jie Ouyang",
"Mingyue Cheng",
"Shijin Wang"
] |
Spotlight
|
Arena-based evaluation is a fundamental yet significant evaluation paradigm for modern AI models, especially large language models (LLMs). Existing framework based on ELO rating system suffers from the inevitable instability problem due to ranking inconsistency and the lack of attention to the varying abilities of annotators. In this paper, we introduce a novel stable arena framework to address these issues by enhancing the ELO Rating System. Specifically, we replace the iterative update method with a Maximum Likelihood Estimation (MLE) approach, m-ELO, and provide theoretical proof of the consistency and stability of the MLE approach for model ranking. Additionally, we proposed the am-ELO, which modify the Elo Rating’s probability function to incorporate annotator abilities, enabling the simultaneous estimation of model scores and annotator reliability. Experiments demonstrate that this method ensures stability, proving that this framework offers a more robust, accurate, and stable evaluation method for LLMs.
|
Large Language Models, Evaluation, Chatbot Arena, ELO Rating System
| null | 4,626
| null |
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] | null |
3,206
|
Nonparametric Teaching for Graph Property Learners
|
https://openreview.net/forum?id=wbvshlfyB0
|
[
"Chen Zhang",
"Weixin Bu",
"Zeyi Ren",
"Zhengwu Liu",
"Yik Chung WU",
"Ngai Wong"
] |
Spotlight
|
Inferring properties of graph-structured data, *e.g.*, the solubility of molecules, essentially involves learning the implicit mapping from graphs to their properties. This learning process is often costly for graph property learners like Graph Convolutional Networks (GCNs). To address this, we propose a paradigm called Graph Nonparametric Teaching (GraNT) that reinterprets the learning process through a novel nonparametric teaching perspective. Specifically, the latter offers a theoretical framework for teaching implicitly defined (*i.e.*, nonparametric) mappings via example selection. Such an implicit mapping is realized by a dense set of graph-property pairs, with the GraNT teacher selecting a subset of them to promote faster convergence in GCN training. By analytically examining the impact of graph structure on parameter-based gradient descent during training, and recasting the evolution of GCNs—shaped by parameter updates—through functional gradient descent in nonparametric teaching, we show *for the first time* that teaching graph property learners (*i.e.*, GCNs) is consistent with teaching structure-aware nonparametric learners. These new findings readily commit GraNT to enhancing learning efficiency of the graph property learner, showing significant reductions in training time for graph-level regression (-36.62\%), graph-level classification (-38.19\%), node-level regression (-30.97\%) and node-level classification (-47.30\%), all while maintaining its generalization performance.
|
Nonparametric Teaching, Graph Property Learning, Functional Gradient Descent
| null | 4,554
|
2505.14170
|
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] |
https://github.com/chen2hang/GraNT_NonparametricTeaching
|
3,207
|
Discovering a Zero (Zero-Vector Class of Machine Learning)
|
https://openreview.net/forum?id=u3n5wuRGTa
|
[
"Harikrishna Metta",
"Venkatesh Babu Radhakrishnan"
] |
Spotlight
|
In Machine learning, separating data into classes is a very fundamental problem. A mathematical framework around the classes is presented in this work to deepen the understanding of classes. The classes are defined as vectors in a Vector Space, where addition corresponds to the union of classes, and scalar multiplication resembles set complement of classes. The Zero-Vector in the vector space corresponds to a class referred to as the Metta-Class. This discovery enables numerous applications. One such application, termed 'clear learning' in this work, focuses on learning the true nature (manifold) of the data instead of merely learning a boundary sufficient for classification. Another application, called 'unary class learning', involves learning a single class in isolation rather than learning by comparing two or more classes. Additionally, 'set operations on classes' is another application highlighted in this work. Furthermore, Continual Learning of classes is facilitated by smaller networks. The Metta-Class enables neural networks to learn only the data manifold; therefore, it can also be used for generation of new data. Results for the key applications are shown using the MNIST dataset. To further strengthen the claims, some results are also produced using the CIFAR-10 and ImageNet-1k embeddings. The code supporting these applications is publicly available at: github.com/hm-4/Metta-Class.
|
Metta, Metta-Class, Metta Class, Machine Learning, ICML, Class Vector, Class Tensor Equation, Class Integration, Repository of Classes, Continual Learning, Class Addition, Class Subtraction, Class Invert, Zero Vector Class, Set operations on Classes, Boolean operation on Classes, Unary classification, Manifold learning, Lifelong Learning, Classifier as Generator
|
The classes are defined as vectors in a Vector Space, where Addition corresponds to the union of classes, and scalar multiplication resembles set complement of classes. Zero-Vector in that vector space has many useful applications.
| 4,514
| null |
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] |
https://github.com/hm-4/Metta-Class
|
3,208
|
Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models
|
https://openreview.net/forum?id=V61nluxFlR
|
[
"Yinhong Liu",
"Zhijiang Guo",
"Tianya Liang",
"Ehsan Shareghi",
"Ivan Vulić",
"Nigel Collier"
] |
Spotlight
|
Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems. Yet current LLMs often show inconsistencies in their judgments. In this work, we examine \textit{logical preference consistency} as a foundational requirement for building more dependable LLM systems, ensuring stable and coherent decision-making while minimizing erratic or contradictory outputs.
To quantify the logical preference consistency, we propose a universal evaluation framework based on three fundamental properties: *transitivity*, *commutativity* and *negation invariance*.
Through extensive experimentation across diverse LLMs, we demonstrate that these properties serve as strong indicators of judgment robustness.
Furthermore, we introduce a data refinement and augmentation technique, REPAIR, that enhances logical consistency while maintaining alignment with human preferences. Finally, we show that improving consistency leads to better performance in LLM-driven logic-based algorithms, reinforcing stability and coherence in decision-making systems.
|
LLMs, logical consistency, order consistency, transitivity
|
We quantify, evaluate and improve the logical preference consistency of LLMs' judgements
| 4,461
|
2410.02205
|
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] | null |
3,209
|
Better to Teach than to Give: Domain Generalized Semantic Segmentation via Agent Queries with Diffusion Model Guidance
|
https://openreview.net/forum?id=jvP1wbD0xh
|
[
"Fan Li",
"Xuan Wang",
"Min Qi",
"Zhaoxiang Zhang",
"yuelei xu"
] |
Spotlight
|
Domain Generalized Semantic Segmentation (DGSS) trains a model on a labeled source domain to generalize to unseen target domains with consistent contextual distribution and varying visual appearance.
Most existing methods rely on domain randomization or data generation but struggle to capture the underlying scene distribution, resulting in the loss of useful semantic information.
Inspired by the diffusion model's capability to generate diverse variations within a given scene context, we consider harnessing its rich prior knowledge of scene distribution to tackle the challenging DGSS task.
In this paper, we propose a novel agent \textbf{Query}-driven learning framework based on \textbf{Diff}usion model guidance for DGSS, named QueryDiff.
Our recipe comprises three key ingredients: (1) generating agent queries from segmentation features to aggregate semantic information about instances within the scene;
(2) learning the inherent semantic distribution of the scene through agent queries guided by diffusion features;
(3) refining segmentation features using optimized agent queries for robust mask predictions.
Extensive experiments across various settings demonstrate that our method significantly outperforms previous state-of-the-art methods.
Notably, it enhances the model's ability to generalize effectively to extreme domains, such as cubist art styles. Code is available at https://github.com/FanLiHub/QueryDiff.
|
semantic segmentation, domain generalization, diffusion model
| null | 4,280
| null |
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] | null |
3,210
|
P(all-atom) Is Unlocking New Path For Protein Design
|
https://openreview.net/forum?id=yXRixu0ONY
|
[
"Wei Qu",
"Jiawei Guan",
"Rui Ma",
"kezhai",
"Weikun.Wu",
"Haobo Wang"
] |
Spotlight
|
We introduce Pallatom, an innovative protein generation model capable of producing protein structures with all-atom coordinates. Pallatom directly learns and models the joint distribution $P(\textit{structure}, \textit{seq})$ by focusing on $P(\textit{all-atom})$, effectively addressing the interdependence between sequence and structure in protein generation. To achieve this, we propose a novel network architecture specifically designed for all-atom protein generation. Our model employs a dual-track framework that tokenizes proteins into token-level and atomic-level representations, integrating them through a multi-layer decoding process with "traversing" representations and recycling mechanism. We also introduce the $\texttt{atom14}$ representation method, which unifies the description of unknown side-chain coordinates, ensuring high fidelity between the generated all-atom conformation and its physical structure. Experimental results demonstrate that Pallatom excels in key metrics of protein design, including designability, diversity, and novelty, showing significant improvements across the board. Our model not only enhances the accuracy of protein generation but also exhibits excellent sampling efficiency, paving the way for future applications in larger and more complex systems.
|
Proteins, Generative models, Co-design, All-atom
|
A state-of- the-art all-atom protein generative model.
| 4,192
| null |
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] |
https://github.com/levinthal/Pallatom
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3,211
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The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
|
https://openreview.net/forum?id=I4jNAbqHnM
|
[
"Pedro Pinto Santos",
"Alberto Sardinha",
"Francisco S. Melo"
] |
Spotlight
|
The general-utility Markov decision processes (GUMDPs) framework generalizes the MDPs framework by considering objective functions that depend on the frequency of visitation of state-action pairs induced by a given policy. In this work, we contribute with the first analysis on the impact of the number of trials, i.e., the number of randomly sampled trajectories, in infinite-horizon GUMDPs. We show that, as opposed to standard MDPs, the number of trials plays a key-role in infinite-horizon GUMDPs and the expected performance of a given policy depends, in general, on the number of trials. We consider both discounted and average GUMDPs, where the objective function depends, respectively, on discounted and average frequencies of visitation of state-action pairs. First, we study policy evaluation under discounted GUMDPs, proving lower and upper bounds on the mismatch between the finite and infinite trials formulations for GUMDPs. Second, we address average GUMDPs, studying how different classes of GUMDPs impact the mismatch between the finite and infinite trials formulations. Third, we provide a set of empirical results to support our claims, highlighting how the number of trajectories and the structure of the underlying GUMDP influence policy evaluation.
|
Planning, sequential decision-making, general-utility markov decision processes, convex markov decision processes
| null | 4,103
|
2409.15128
|
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] |
https://github.com/PPSantos/gumdps-number-of-trials
|
3,212
|
On the Benefits of Active Data Collection in Operator Learning
|
https://openreview.net/forum?id=hYHczNrKoX
|
[
"Unique Subedi",
"Ambuj Tewari"
] |
Spotlight
|
We study active data collection strategies for operator learning when the target operator is linear and the input functions are drawn from a mean-zero stochastic process with continuous covariance kernels. With an active data collection strategy, we establish an error convergence rate in terms of the decay rate of the eigenvalues of the covariance kernel. We can achieve arbitrarily fast error convergence rates with sufficiently rapid eigenvalue decay of the covariance kernels. This contrasts with the
passive (i.i.d.) data collection strategies, where the convergence rate is never faster than linear decay ($\sim n^{-1}$). In fact, for our setting, we show a \emph{non-vanishing} lower bound for any passive data collection strategy, regardless of the eigenvalues decay rate of the covariance kernel. Overall, our results show the benefit of active data collection strategies in operator learning over their passive counterparts.
|
Operator Learning, Active Learning, PDEs
|
We study active data collection strategies for operator learning and establish their provable advantage over passive sampling approaches.
| 4,078
|
2410.19725
|
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] |
https://github.com/unique-subedi/active-operator-learning
|
3,213
|
Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger
|
https://openreview.net/forum?id=DJcEoC9JpQ
|
[
"Qi Yang",
"Chenghao Zhang",
"Lubin Fan",
"Kun Ding",
"Jieping Ye",
"Shiming Xiang"
] |
Spotlight
|
Recent advancements in Large Vision Language Models (LVLMs) have significantly improved performance in Visual Question Answering (VQA) tasks through multimodal Retrieval-Augmented Generation (RAG). However, existing methods still face challenges, such as the scarcity of knowledge with reasoning examples and erratic responses from retrieved knowledge. To address these issues, in this study, we propose a multimodal RAG framework, termed RCTS, which enhances LVLMs by constructing a Reasoning Context-enriched knowledge base and a Tree Search re-ranking method. Specifically, we introduce a self-consistent evaluation mechanism to enrich the knowledge base with intrinsic reasoning patterns. We further propose a Monte Carlo Tree Search with Heuristic Rewards (MCTS-HR) to prioritize the most relevant examples. This ensures that LVLMs can leverage high-quality contextual reasoning for better and more consistent responses. Extensive experiments demonstrate that our framework achieves state-of-the-art performance on multiple VQA datasets, significantly outperforming In-Context Learning (ICL) and Vanilla-RAG methods. It highlights the effectiveness of our knowledge base and re-ranking method in improving LVLMs.
|
Large Vision Language Model, Multimodal Retrieval-Augmented Generation, In-context Learning, Monte Carlo Tree Search
|
We propose RCTS, a multimodal RAG framework that enhances LVLMs for VQA tasks by integrating a reasoning-context-enriched knowledge base and tree-search re-ranking, achieving state-of-the-art performance.
| 3,925
|
2506.07785
|
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] |
https://github.com/yannqi/RCTS-RAG
|
3,214
|
Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime
|
https://openreview.net/forum?id=ZrhGq664om
|
[
"Diyuan Wu",
"Marco Mondelli"
] |
Spotlight
|
Neural Collapse is a phenomenon where the last-layer representations of a well-trained neural network converge to a highly structured geometry. In this paper, we focus on its first (and most basic) property, known as NC1: the within-class variability vanishes.
While prior theoretical studies establish the occurrence of NC1 via the data-agnostic unconstrained features model, our work adopts a data-specific perspective, analyzing NC1 in a three-layer neural network, with the first two layers operating in the mean-field regime and followed by a linear layer. In particular, we establish a fundamental connection between NC1 and the loss landscape: we prove that points with small empirical loss and gradient norm (thus, close to being stationary) approximately satisfy NC1, and the closeness to NC1 is
controlled by the residual loss and gradient norm. We then show that (i) gradient flow on the mean squared error converges to NC1 solutions with small empirical loss, and (ii) for well-separated data distributions, both NC1 and vanishing test loss are achieved simultaneously. This aligns with the empirical observation that NC1 emerges during training while models attain near-zero test error. Overall, our results demonstrate that NC1 arises from gradient training due to the properties of the loss landscape, and they show the co-occurrence of NC1 and small test error for certain data distributions.
|
neural collapse, mean-field analysis, gradient flow, generalization error, loss landscape
|
We prove that NC1 (vanishing within-class variability) holds when training a class of 3-layer networks via gradient flow, due to loss landscape properties; we further show co-occurrence of NC1 and small test error for certain data distributions.
| 3,886
|
2501.19104
|
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] |
https://github.com/DiyuanWu/icml25_expr
|
3,215
|
Invariant Deep Uplift Modeling for Incentive Assignment in Online Marketing via Probability of Necessity and Sufficiency
|
https://openreview.net/forum?id=mruyFvKDKq
|
[
"Zexu Sun",
"Qiyu Han",
"Hao Yang",
"Anpeng Wu",
"Minqin Zhu",
"Dugang Liu",
"Chen Ma",
"Yunpeng Weng",
"Xing Tang",
"xiuqiang He"
] |
Spotlight
|
In online platforms, incentives (\textit{e.g}., discounts, coupons) are used to boost user engagement and revenue. Uplift modeling methods are developed to estimate user responses from observational data, often incorporating distribution balancing to address selection bias. However, these methods are limited by in-distribution testing data, which mirrors the training data distribution. In reality, user features change continuously due to time, geography, and other factors, especially on complex online marketing platforms. Thus, effective uplift modeling method for out-of-distribution data is crucial. To address this, we propose a novel uplift modeling method \textbf{I}nvariant \textbf{D}eep \textbf{U}plift \textbf{M}odeling, namely \textbf{IDUM}, which uses invariant learning to enhance out-of-distribution generalization by identifying causal factors that remain consistent across domains. IDUM further refines these features into necessary and sufficient factors and employs a masking component to reduce computational costs by selecting the most informative invariant features. A balancing discrepancy component is also introduced to mitigate selection bias in observational data. We conduct extensive experiments on public and real-world datasets to demonstrate IDUM's effectiveness in both in-distribution and out-of-distribution scenarios in online marketing. Furthermore, we also provide theoretical analysis and related proofs to support our IDUM's generalizability.
|
Uplift modeling, Invariant learning, Incentives assignment, Online marketing
|
This paper proposes an invariant learning based uplift modeling method, which aims to solve the out-of-distribution problem in online marketing.
| 3,824
| null |
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] | null |
3,216
|
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
|
https://openreview.net/forum?id=LpE54NUnmO
|
[
"Guibin Zhang",
"Yanwei Yue",
"Xiangguo Sun",
"Guancheng Wan",
"Miao Yu",
"Junfeng Fang",
"Kun Wang",
"Tianlong Chen",
"Dawei Cheng"
] |
Spotlight
|
Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies. Despite the diverse and high-performing designs available, practitioners often face confusion when selecting the most effective pipeline for their specific task: \textit{Which topology is the best choice for my task, avoiding unnecessary communication token overhead while ensuring high-quality solution?} In response to this dilemma, we introduce G-Designer, an adaptive, efficient, and robust solution for multi-agent deployment, which dynamically designs task-aware, customized communication topologies. Specifically, G-Designer models the multi-agent system as a multi-agent network, leveraging a variational graph auto-encoder to encode both the nodes (agents) and a task-specific virtual node, and decodes a task-adaptive and high-performing communication topology. Extensive experiments on six benchmarks showcase that G-Designer is: \textbf{(1) high-performing}, achieving superior results on MMLU with accuracy at $84.50\\%$ and on HumanEval with pass@1 at $89.90\\%$; \textbf{(2) task-adaptive}, architecting communication protocols tailored to task difficulty, reducing token consumption by up to $95.33\\%$ on HumanEval; and \textbf{(3) adversarially robust}, defending against agent adversarial attacks with merely $0.3\\%$ accuracy drop.
|
Multi-agent communication, Graph machine learning, LLM-based agent
| null | 3,779
| null |
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] |
https://github.com/yanweiyue/GDesigner
|
3,217
|
SAFE: Finding Sparse and Flat Minima to Improve Pruning
|
https://openreview.net/forum?id=10l1pGeOcK
|
[
"Dongyeop Lee",
"Kwanhee Lee",
"Jinseok Chung",
"Namhoon Lee"
] |
Spotlight
|
Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.
Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.
Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective.
We solve it explicitly via an augmented Lagrange dual approach and extend it further by proposing a generalized projection operation, resulting in novel pruning methods called SAFE and its extension, SAFE$^+$.
Extensive evaluations on standard image classification and language modeling tasks reveal that SAFE consistently yields sparse networks with improved generalization performance, which compares competitively to well-established baselines.
In addition, SAFE demonstrates resilience to noisy data, making it well-suited for real-world conditions.
|
Pruning, Constrained optimization, Sharpness minimization
|
We propose SAFE, an optimization-based pruning method that improves generalization of sparse models by inducing flatness.
| 3,619
|
2506.06866
|
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] |
https://github.com/LOG-postech/safe-torch,https://github.com/LOG-postech/safe-jax
|
3,218
|
On the Guidance of Flow Matching
|
https://openreview.net/forum?id=pKaNgFzJBy
|
[
"Ruiqi Feng",
"Chenglei Yu",
"Wenhao Deng",
"Peiyan Hu",
"Tailin Wu"
] |
Spotlight
|
Flow matching has shown state-of-the-art performance in various generative tasks, ranging from image generation to decision-making, where generation under energy guidance (abbreviated as guidance in the following) is pivotal. However, the guidance of flow matching is more general than and thus substantially different from that of its predecessor, diffusion models. Therefore, the challenge in guidance for general flow matching remains largely underexplored. In this paper, we propose the first framework of general guidance for flow matching. From this framework, we derive a family of guidance techniques that can be applied to general flow matching. These include a new training-free asymptotically exact guidance, novel training losses for training-based guidance, and two classes of approximate guidance that cover classical gradient guidance methods as special cases. We theoretically investigate these different methods to give a practical guideline for choosing suitable methods in different scenarios. Experiments on synthetic datasets, image inverse problems, and offline reinforcement learning demonstrate the effectiveness of our proposed guidance methods and verify the correctness of our flow matching guidance framework. Code to reproduce the experiments can be found at https://github.com/AI4Science-WestlakeU/flow_guidance.
|
flow matching, guided generation, generative modeling
|
We introduce the first framework for general flow matching guidance, from which new guidance methods are derived and many classical guidance methods are covered as special cases.
| 3,557
|
2502.02150
|
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] |
https://github.com/AI4Science-WestlakeU/flow_guidance
|
3,219
|
TLLC: Transfer Learning-based Label Completion for Crowdsourcing
|
https://openreview.net/forum?id=BkdAnSKNoX
|
[
"Wenjun Zhang",
"Liangxiao Jiang",
"Chaoqun Li"
] |
Spotlight
|
Label completion serves as a preprocessing approach to handling the sparse crowdsourced label matrix problem, significantly boosting the effectiveness of the downstream label aggregation. In recent advances, worker modeling has been proved to be a powerful strategy to further improve the performance of label completion. However, in real-world scenarios, workers typically annotate only a few instances, leading to insufficient worker modeling and thus limiting the improvement of label completion. To address this issue, we propose a novel transfer learning-based label completion (TLLC) method. Specifically, we first identify all high-confidence instances from the whole crowdsourced data as a source domain and use it to pretrain a Siamese network. The abundant annotated instances in the source domain provide essential knowledge for worker modeling. Then, we transfer the pretrained network to the target domain with the instances annotated by each worker separately, ensuring worker modeling captures unique characteristics of each worker. Finally, we leverage the new embeddings learned by the transferred network to complete each worker’s missing labels. Extensive experiments on several widely used real-world datasets demonstrate the effectiveness of TLLC. Our codes and datasets are available at https://github.com/jiangliangxiao/TLLC.
|
Crowdsourcing learning, Label Completion, Worker modeling, Transfer Learning
|
This paper proposes a novel transfer learning-based label completion (TLLC) algorithm.
| 3,322
| null |
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] |
https://github.com/jiangliangxiao/TLLC
|
3,220
|
Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces
|
https://openreview.net/forum?id=U354tbTjav
|
[
"Henry Moss",
"Sebastian W. Ober",
"Tom Diethe"
] |
Spotlight
|
Bayesian optimisation in the latent space of a VAE is a powerful framework for optimisation tasks over complex structured domains, such as the space of valid molecules. However, existing approaches tightly couple the surrogate and generative models, which can lead to suboptimal performance when the latent space is not tailored to specific tasks, which in turn has led to the proposal of increasingly sophisticated algorithms. In this work, we explore a new direction, instead proposing a decoupled approach that trains a generative model and a GP surrogate separately, then combines them via a simple yet principled Bayesian update rule. This separation allows each component to focus on its strengths— structure generation from the VAE and predictive modelling by the GP. We show that our decoupled approach improves our ability to identify high-potential candidates in molecular optimisation problems under constrained evaluation budgets.
|
Bayesian Optimisation
|
Don't do Bayesian optimisation in the latent space of a VAE ....
| 3,247
|
2507.03910
|
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] | null |
3,221
|
New Bounds for Sparse Variational Gaussian Processes
|
https://openreview.net/forum?id=Ppcf30NGL0
|
[
"Michalis Titsias"
] |
Spotlight
|
Sparse variational Gaussian processes (GPs) construct tractable posterior approximations to GP models. At the core of these methods is the assumption that the true posterior distribution over training function values ${\bf f}$ and inducing variables ${\bf u}$ is approximated by a variational distribution that incorporates the conditional GP prior $p({\bf f} | {\bf u})$ in its factorization. While this assumption is considered as fundamental, we show that for model training we can relax it through the use of a more general variational distribution $q({\bf f} | {\bf u} )$ that depends on $N$ extra parameters, where $N$ is the number of training examples. In GP regression, we can analytically optimize the evidence lower bound over the extra parameters and express a tractable collapsed bound that is tighter than the previous bound. The new bound is also amenable to stochastic optimization and its implementation requires minor modifications to existing sparse GP code. Further, we also describe extensions to non-Gaussian likelihoods. On several datasets we demonstrate that our method can reduce bias when learning the hyperparameters and can lead to better predictive performance.
|
Sparse variational Gaussian process, new collapsed bound
|
It presents new collapsed and uncollapsed bounds for sparse variational Gaussian processes using inducing points.
| 3,219
|
2502.08730
|
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] | null |
3,222
|
An Error Analysis of Flow Matching for Deep Generative Modeling
|
https://openreview.net/forum?id=vES22INUKm
|
[
"Zhengyu Zhou",
"Weiwei Liu"
] |
Spotlight
|
Continuous Normalizing Flows (CNFs) have proven to be a highly efficient technique for generative modeling of complex data since the introduction of Flow Matching (FM). The core of FM is to learn the constructed velocity fields of CNFs through deep least squares regression. Despite its empirical effectiveness, theoretical investigations of FM remain limited. In this paper, we present the first end-to-end error analysis of CNFs built upon FM. Our analysis shows that for general target distributions with bounded support, the generated distribution of FM is guaranteed to converge to the target distribution in the sense of the Wasserstein-2 distance. Furthermore, the convergence rate is significantly improved under an additional mild Lipschitz condition of the target score function.
|
Statistical Learning Theory
| null | 3,098
| null |
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] | null |
3,223
|
Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance
|
https://openreview.net/forum?id=PUzNwYmb3l
|
[
"Lisha Chen",
"Quan Xiao",
"Ellen Hidemi Fukuda",
"Xinyi Chen",
"Kun Yuan",
"Tianyi Chen"
] |
Spotlight
|
Multi-objective learning under user-specified preference is common in real-world problems such as multi-lingual speech recognition under fairness. In this work, we frame such a problem as a semivectorial bilevel optimization problem, whose goal is to optimize a pre-defined preference function, subject to the constraint that the model parameters are weakly Pareto optimal. To solve this problem, we convert the multi-objective constraints to a single-objective constraint through a merit function with an easy-to-evaluate gradient, and then, we use a penalty-based reformulation of the bilevel optimization problem. We theoretically establish the properties of the merit function, and the relations of solutions for the penalty reformulation and the constrained formulation. Then we propose algorithms to solve the reformulated single-level problem, and establish its convergence guarantees. We test the method on various synthetic and real-world problems. The results demonstrate the effectiveness of the proposed method in finding preference-guided optimal solutions to the multi-objective problem.
|
multi-objective optimization, optimization on the Pareto set, semivectorial bilevel optimization
|
We cast the preference-guided multi-objective learning problem as optimization on the Pareto set, and propose a first-order penalty approach to solve it.
| 3,045
|
2504.02854
|
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] | null |
3,224
|
Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
|
https://openreview.net/forum?id=iFOXz5H2gB
|
[
"Xihong Yang",
"Siwei Wang",
"Fangdi Wang",
"Jiaqi Jin",
"Suyuan Liu",
"Yue Liu",
"En Zhu",
"Xinwang Liu",
"Yueming Jin"
] |
Spotlight
|
Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source information from diverse views in recent years. Most existing methods rely on the assumption of clean views. However, noise is pervasive in real-world scenarios, leading to a significant degradation in performance. To tackle this problem, we propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC. Specifically, we reformulate noisy identification as an anomaly identification problem using GMM. We then design a hybrid rectification strategy to mitigate the adverse effects of noisy data based on the identification results. Furthermore, we introduce a noise-robust contrastive mechanism to generate reliable representations. Additionally, we provide a theoretical proof demonstrating that these representations can discard noisy information, thereby improving the performance of downstream tasks. Extensive experiments on six benchmark datasets demonstrate that AIRMVC outperforms state-of-the-art algorithms in terms of robustness in noisy scenarios. The code of AIRMVC are available at https://github.com/xihongyang1999/AIRMVC on Github.
|
Multi-view Clustering; Contrastive Learning; Noisy Scenarios
| null | 2,848
|
2505.21387
|
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] | null |
3,225
|
Improving Zero-Shot Adversarial Robustness in Vision-Language Models by Closed-form Alignment of Adversarial Path Simplices
|
https://openreview.net/forum?id=WR0ahlhOoy
|
[
"Junhao Dong",
"Piotr Koniusz",
"Yifei Zhang",
"Hao Zhu",
"Weiming Liu",
"Xinghua Qu",
"Yew-Soon Ong"
] |
Spotlight
|
Vision-Language Models (VLMs) such as CLIP excel at zero-shot classification due to large-scale pre-training but are vulnerable to adversarial examples. Adversarial fine-tuning robustifies zero-shot models by aligning prediction scores of individual adversaries with their clean counterparts, which typically overlooks intermediate adversarial samples along the adversarial trajectory crossing the decision boundary. Such intermediate adversaries and their vicinity produce informative representations capturing the decision boundary in detail. They can be improved by sampling adversarial candidates from simplices formed by joining two consecutive vertices on the adversarial trajectory and their clean counterpart. However, sampling simplices for adversaries is very costly. To train robust VLM, we overcome these limitations by Taylor expansion and formulating an upper-bound of alignment loss that depends on the Jacobian/Hessian obtained at clean samples. As regions between clean and intermediate adversarial samples capture a larger decision landscape, we robustify VLM by plausible adversaries from simplices by our closed-form formulation equivalent to infinite uniform sampling of the simplex. We obtain state-of-the-art robustness across 15 datasets and diverse vision-language tasks.
|
Vision-Language Models, Adversarial Examples, Zero-Shot Classification
|
We robustify VLMs by aligning entire adversarial simplices rather than individual adversarial samples with classifier scores of clean samples.
| 2,781
| null |
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] | null |
3,226
|
Graph Diffusion for Robust Multi-Agent Coordination
|
https://openreview.net/forum?id=T5IZ32ImAB
|
[
"Xianghua Zeng",
"Hang Su",
"Zhengyi Wang",
"Zhiyuan LIN"
] |
Spotlight
|
Offline multi-agent reinforcement learning (MARL) struggles to estimate out-of-distribution states and actions due to the absence of real-time environmental feedback. While diffusion models show promise in addressing these challenges, their application primarily focuses on independently diffusing the historical trajectories of individual agents, neglecting crucial multi-agent coordination dynamics and reducing policy robustness in dynamic environments. In this paper, we propose MCGD, a novel Multi-agent Coordination framework based on Graph Diffusion models to improve the effectiveness and robustness of collaborative policies. Specifically, we begin by constructing a sparse coordination graph that includes continuous node attributes and discrete edge attributes to effectively identify the underlying dynamics of multi-agent interactions. Next, we derive transition probabilities between edge categories and present adaptive categorical diffusion to capture the structure diversity of multi-agent coordination. Leveraging this coordination structure, we define neighbor-dependent forward noise and develop anisotropic diffusion to enhance the action diversity of each agent. Extensive experiments across various multi-agent environments demonstrate that MCGD significantly outperforms existing state-of-the-art baselines in coordination performance and policy robustness in dynamic environments.
|
multi-agent coordination, offline reinforcement learning, diffusion models
| null | 2,772
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] | null |
3,227
|
Weakly-Supervised Contrastive Learning for Imprecise Class Labels
|
https://openreview.net/forum?id=Y19ngWhN0b
|
[
"Zi-Hao Zhou",
"Jun-Jie Wang",
"Tong Wei",
"Min-Ling Zhang"
] |
Spotlight
|
Contrastive learning has achieved remarkable success in learning effective representations, with supervised contrastive learning often outperforming self-supervised approaches. However, in real-world scenarios, data annotations are often ambiguous or inaccurate, meaning that class labels may not reliably indicate whether two examples belong to the same class. This limitation restricts the applicability of supervised contrastive learning. To address this challenge, we introduce the concept of ``continuous semantic similarity'' to define positive and negative pairs. Instead of directly relying on imprecise class labels, we measure the semantic similarity between example pairs, which quantifies how closely they belong to the same category by iteratively refining weak supervisory signals. Based on this concept, we propose a graph-theoretic framework for weakly-supervised contrastive learning, where semantic similarity serves as the graph weights. Our framework is highly versatile and can be applied to many weakly-supervised learning scenarios. We demonstrate its effectiveness through experiments in two common settings, i.e., noisy label and partial label learning, where existing methods can be easily integrated to significantly improve performance. Theoretically, we establish an error bound for our approach, showing that it can approximate supervised contrastive learning under mild conditions. The implementation code is available at [https://github.com/Speechless-10308/WSC](https://github.com/Speechless-10308/WSC).
|
Weakly-supervised learning, Contrastive learning, Noisy label learning, Partial label learning
| null | 2,740
|
2505.22028
|
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] |
https://github.com/Speechless-10308/WSC
|
3,228
|
Robust Automatic Modulation Classification with Fuzzy Regularization
|
https://openreview.net/forum?id=DDIGCk25BO
|
[
"Xinyan Liang",
"Ruijie Sang",
"Yuhua Qian",
"Qian Guo",
"Feijiang Li",
"Liang Du"
] |
Spotlight
|
Automatic Modulation Classification (AMC) serves as a foundational pillar for cognitive radio systems, enabling critical functionalities including dynamic spectrum allocation, non-cooperative signal surveillance, and adaptive waveform optimization. However, practical deployment of AMC faces a fundamental challenge: prediction ambiguity arising from intrinsic similarity among modulation schemes and exacerbated under low signal-to-noise ratio (SNR) conditions. This phenomenon manifests as near-identical probability distributions across confusable modulation types, significantly degrading classification reliability. To address this, we propose Fuzzy Regularization-enhanced AMC (FR-AMC), a novel framework that integrates uncertainty quantification into the classification pipeline. The proposed FR has three features: (1) Explicitly model prediction ambiguity during backpropagation, (2) dynamic sample reweighting through adaptive loss scaling, (3) encourage margin maximization between confusable modulation clusters. Experimental results on benchmark datasets demonstrate that the FR achieves superior classification accuracy and robustness compared to compared methods, making it a promising solution for real-world spectrum management and communication applications.
|
Robustness, Fuzzy Regularization, Automatic Modulation Classification
| null | 2,696
| null |
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] |
https://github.com/ruijiesang/FR-AMC
|
3,229
|
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
|
https://openreview.net/forum?id=bLcXkIasck
|
[
"Ken Liu",
"Christopher A. Choquette-Choo",
"Matthew Jagielski",
"Peter Kairouz",
"Sanmi Koyejo",
"Percy Liang",
"Nicolas Papernot"
] |
Spotlight
|
An important question today is whether a given text was used to train a large language model (LLM). A completion test is often employed: check if the LLM completes a sufficiently complex text. This, however, requires a ground-truth definition of membership; most commonly, it is defined as a member based on the n-gram overlap between the target text and any text in the dataset. In this work, we demonstrate that this n-gram based membership definition can be effectively gamed. We study scenarios where sequences are non-members for a given n and we find that completion tests still succeed. We find many natural cases of this phenomenon by retraining LLMs from scratch after removing all training samples that were completed; these cases include exact duplicates, near-duplicates, and even short overlaps. They showcase that it is difficult to find a single viable choice of n for membership definitions. Using these insights, we design adversarial datasets that can cause a given target sequence to be completed without containing it, for any reasonable choice of n. Our findings highlight the inadequacy of n-gram membership, suggesting membership definitions fail to account for auxiliary information available to the training algorithm.
|
Training data membership, data completion, data reconstruction, membership inference, unlearning, privacy, training set inclusion, copyright
|
Under $n$-gram definitions of train-set inclusion, LLMs can complete “unseen” texts—both after data deletion and adding “gibberish” data. Our results impact unlearning, membership inference & data transparency.
| 2,670
|
2503.17514
|
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0.043636810034513474,
-0.023663997650146484,
0.012012009508907795,
-0.036431070417165756
] | null |
3,230
|
Taming Knowledge Conflicts in Language Models
|
https://openreview.net/forum?id=0cEZyhHEks
|
[
"Gaotang Li",
"Yuzhong Chen",
"Hanghang Tong"
] |
Spotlight
|
Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge.
Previous works attribute this conflict to the interplay between "memory heads" and "context heads", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the *superposition of contextual information and parametric memory*, where highly influential attention heads simultaneously contribute to both memory and context. Building upon this insight, we propose Just Run Twice (JuICE), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Extensive experiments across 11 datasets and 6 model architectures demonstrate that JuICE sets the new state-of-the-art performance and robust generalization, achieving significant and consistent improvement across different domains under various conflict types. Finally, we theoretically analyze knowledge conflict and the superposition of contextual information and parametric memory in attention heads, which further elucidates the effectiveness of JuICE in these settings. Our code is available at https://github.com/GaotangLi/JUICE.
|
Knowledge Conflict, Mechanistic Interpretability, Science of Large Language Models
| null | 2,610
|
2503.10996
|
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] |
https://github.com/GaotangLi/JUICE
|
3,231
|
UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control
|
https://openreview.net/forum?id=uqCfoVXb67
|
[
"Kaizhen Zhu",
"Mokai Pan",
"Yuexin Ma",
"Yanwei Fu",
"Jingyi Yu",
"Jingya Wang",
"Ye Shi"
] |
Spotlight
|
Recent advances in diffusion bridge models leverage Doob’s $h$-transform to establish fixed endpoints between distributions, demonstrating promising results in image translation and restoration tasks. However, these approaches frequently produce blurred or excessively smoothed image details and lack a comprehensive theoretical foundation to explain these shortcomings. To address these limitations, we propose UniDB, a unified framework for diffusion bridges based on Stochastic Optimal Control (SOC). UniDB formulates the problem through an SOC-based optimization and derives a closed-form solution for the optimal controller, thereby unifying and generalizing existing diffusion bridge models. We demonstrate that existing diffusion bridges employing Doob’s $h$-transform constitute a special case of our framework, emerging when the terminal penalty coefficient in the SOC cost function tends to infinity. By incorporating a tunable terminal penalty coefficient, UniDB achieves an optimal balance between control costs and terminal penalties, substantially improving detail preservation and output quality. Notably, UniDB seamlessly integrates with existing diffusion bridge models, requiring only minimal code modifications. Extensive experiments across diverse image restoration tasks validate the superiority and adaptability of the proposed framework. Our code is available at https://github.com/UniDB-SOC/UniDB/.
|
Diffusion bridge, Doob's h-transform, Stochastic optimal control
|
We present UniDB, a unified diffusion bridge framework using stochastic optimal control, significantly improving detail preservation and image quality in generative tasks with minimal code modifications.
| 2,519
|
2502.05749
|
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] |
https://github.com/UniDB-SOC/UniDB
|
3,232
|
K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
|
https://openreview.net/forum?id=71Mm8GDGYd
|
[
"Xingjian Wu",
"Xiangfei Qiu",
"Hongfan Gao",
"Jilin Hu",
"Bin Yang",
"Chenjuan Guo"
] |
Spotlight
|
Probabilistic Time Series Forecasting (PTSF) plays a crucial role in decision-making across various fields, including economics, energy, and transportation. Most existing methods excell at short-term forecasting, while overlooking the hurdles of Long-term Probabilistic Time Series Forecasting (LPTSF). As the forecast horizon extends, the inherent nonlinear dynamics have a significant adverse effect on prediction accuracy, and make generative models inefficient by increasing the cost of each iteration. To overcome these limitations, we introduce $K^2$VAE, an efficient VAE-based generative model that leverages a KoopmanNet to transform nonlinear time series into a linear dynamical system, and devises a KalmanNet to refine predictions and model uncertainty in such linear system, which reduces error accumulation in long-term forecasting. Extensive experiments demonstrate that $K^2$VAE outperforms state-of-the-art methods in both short- and long-term PTSF, providing a more efficient and accurate solution.
|
Time Series Probabilistic Forecasting
| null | 2,349
|
2505.23017
|
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] |
https://github.com/decisionintelligence/K2VAE
|
3,233
|
Doubly Robust Conformalized Survival Analysis with Right-Censored Data
|
https://openreview.net/forum?id=2PWn1LtCwP
|
[
"Matteo Sesia",
"Vladimir Svetnik"
] |
Spotlight
|
We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for more restrictive type-I censoring scenarios. The proposed method imputes unobserved censoring times using a machine learning model, and then analyzes the imputed data using a survival model calibrated via weighted conformal inference. This approach is theoretically supported by an asymptotic double robustness property. Empirical studies on simulated and real data demonstrate that our method leads to relatively informative predictive inferences and is especially robust in challenging settings where the survival model may be inaccurate.
|
Conformal inference, Survival analysis, Uncertainty Estimation
|
This paper presents a conformal inference method for constructing lower prediction bounds for survival times from right-censored data.
| 2,249
|
2412.09729
|
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] |
https://github.com/msesia/conformal_survival
|
3,234
|
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
|
https://openreview.net/forum?id=WbP2OwMULq
|
[
"Tianwei Lin",
"Wenqiao Zhang",
"SIJING LI",
"Yuqian Yuan",
"Binhe Yu",
"Haoyuan Li",
"Wanggui He",
"Hao Jiang",
"Mengze Li",
"Song xiaohui",
"Siliang Tang",
"Jun Xiao",
"Hui Lin",
"Yueting Zhuang",
"Beng Chin Ooi"
] |
Spotlight
|
We present **HealthGPT**, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregressive paradigm. Our bootstrapping philosophy is to progressively adapt heterogeneous comprehension and generation knowledge to pre-trained Large Language Models (LLMs). This is achieved through a novel heterogeneous low-rank adaptation **(H-LoRA)** technique, which is complemented by a tailored hierarchical visual perception **(HVP)** approach and a three-stage learning strategy **(TLS)**. To effectively learn the HealthGPT, we devise a comprehensive medical domain-specific comprehension and generation dataset called **VL-Health**. Experimental results demonstrate exceptional performance and scalability
of HealthGPT in medical visual unified tasks. Our project can be accessed at https://github.com/DCDmllm/HealthGPT.
|
Medical Large Vision-Language Models; Multi-Modal Comprehension and Generation
| null | 2,225
|
2502.09838
|
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] |
https://github.com/DCDmllm/HealthGPT
|
3,235
|
TimeBase: The Power of Minimalism in Efficient Long-term Time Series Forecasting
|
https://openreview.net/forum?id=GhTdNOMfOD
|
[
"Qihe Huang",
"Zhengyang Zhou",
"Kuo Yang",
"Zhongchao Yi",
"Xu Wang",
"Yang Wang"
] |
Spotlight
|
Long-term time series forecasting (LTSF) has traditionally relied on large parameters to capture extended temporal dependencies, resulting in substantial computational costs and inefficiencies in both memory usage and processing time.
However, time series data, unlike high-dimensional images or text, often exhibit temporal pattern similarity and low-rank structures, especially in long-term horizons. By leveraging this structure, models can be guided to focus on more essential, concise temporal data, improving both accuracy and computational efficiency. In this paper, we introduce TimeBase, an ultra-lightweight network to harness the power of minimalism in LTSF. TimeBase 1) extracts core basis temporal components and 2) transforms traditional point-level forecasting into efficient segment-level forecasting, achieving optimal utilization of both data and parameters. Extensive experiments on diverse real-world datasets show that TimeBase achieves remarkable efficiency and secures competitive forecasting performance. Additionally, TimeBase can also serve as a very effective plug-and-play complexity reducer for any patch-based forecasting models. Code is available at \url{https://github.com/hqh0728/TimeBase}.
|
Time series forecasting
| null | 2,176
| null |
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] | null |
3,236
|
Catch Your Emotion: Sharpening Emotion Perception in Multimodal Large Language Models
|
https://openreview.net/forum?id=IYOksPHJKT
|
[
"Yiyang Fang",
"Jian Liang",
"Wenke Huang",
"He Li",
"Kehua Su",
"Mang Ye"
] |
Spotlight
|
Multimodal large language models (MLLMs) have achieved impressive progress in tasks such as visual question answering and visual understanding, but they still face significant challenges in emotional reasoning. Current methods to enhance emotional understanding typically rely on fine-tuning or manual annotations, which are resource-intensive and limit scalability. In this work, we focus on improving the ability of MLLMs to capture emotions during the inference phase. Specifically, MLLMs encounter two main issues: they struggle to distinguish between semantically similar emotions, leading to misclassification, and they are overwhelmed by redundant or irrelevant visual information, which distracts from key emotional cues. To address these, we propose Sharpening Emotion Perception in MLLMs (SEPM), which incorporates a Confidence-Guided Coarse-to-Fine Inference framework to refine emotion classification by guiding the model through simpler tasks. Additionally, SEPM employs Focus-on-Emotion Visual Augmentation to reduce visual redundancy by directing the attention of models to relevant emotional cues in images. Experimental results demonstrate that SEPM significantly improves MLLM performance on emotion-related tasks, providing a resource-efficient and scalable solution for emotion recognition.
|
Multimodal Large Language Models, Emotion Recognition, Training-Free
|
We propose SEPM to enhance MLLMs' emotion recognition by refining classification through a two-stage inference and reducing visual redundancy, offering a scalable, resource-efficient solution.
| 2,164
| null |
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] |
https://github.com/fuyyyyy/SEPM
|
3,237
|
Layer-wise Alignment: Examining Safety Alignment Across Image Encoder Layers in Vision Language Models
|
https://openreview.net/forum?id=F1ff8zcjPp
|
[
"Saketh Bachu",
"Erfan Shayegani",
"Rohit Lal",
"Trishna Chakraborty",
"Arindam Dutta",
"Chengyu Song",
"Yue Dong",
"Nael B. Abu-Ghazaleh",
"Amit Roy-Chowdhury"
] |
Spotlight
|
Vision-language models (VLMs) have improved significantly in their capabilities, but their complex architecture makes their safety alignment challenging. In this paper, we reveal an uneven distribution of harmful information across the intermediate layers of the image encoder and show that skipping a certain set of layers and exiting early can increase the chance of the VLM generating harmful responses. We call it as “Image enCoder Early-exiT” based vulnerability (ICET). Our experiments across three VLMs: LLaVA-1.5, LLaVA-NeXT, and Llama 3.2 show that performing early exits from the image encoder significantly increases the likelihood of generating harmful outputs. To tackle this, we propose a simple yet effective modification of the Clipped-Proximal Policy Optimization (Clip-PPO) algorithm for performing layer-wise multi-modal RLHF for VLMs. We term this as Layer-Wise PPO (L-PPO). We evaluate our L-PPO algorithm across three multi-modal datasets and show that it consistently reduces the harmfulness caused by early exits.
|
Vision Language Models, Safety Alignment, Reinforcement Learning from Human Feedback (RLHF)
|
We reveal an image encoder early exit based vulnerability in VLMs and propose layer-wise RLHF to alleviate it.
| 2,098
|
2411.04291
|
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] | null |
3,238
|
Training Dynamics of In-Context Learning in Linear Attention
|
https://openreview.net/forum?id=aFNq67ilos
|
[
"Yedi Zhang",
"Aaditya K Singh",
"Peter E. Latham",
"Andrew M Saxe"
] |
Spotlight
|
While attention-based models have demonstrated the remarkable ability of in-context learning (ICL), the theoretical understanding of how these models acquired this ability through gradient descent training is still preliminary. Towards answering this question, we study the gradient descent dynamics of multi-head linear self-attention trained for in-context linear regression. We examine two parametrizations of linear self-attention: one with the key and query weights merged as a single matrix (common in theoretical studies), and one with separate key and query matrices (closer to practical settings). For the merged parametrization, we show that the training dynamics has two fixed points and the loss trajectory exhibits a single, abrupt drop. We derive an analytical time-course solution for a certain class of datasets and initialization. For the separate parametrization, we show that the training dynamics has exponentially many fixed points and the loss exhibits saddle-to-saddle dynamics, which we reduce to scalar ordinary differential equations. During training, the model implements principal component regression in context with the number of principal components increasing over training time. Overall, we provide a theoretical description of how ICL abilities evolve during gradient descent training of linear attention, revealing abrupt acquisition or progressive improvements depending on how the key and query are parametrized.
|
learning dynamics, in-context learning, linear attention
|
We theoretically characterize how in-context learning abilities evolve during gradient descent training of linear attention, revealing abrupt acquisition or progressive improvements depending on how the key and query are parametrized.
| 1,939
|
2501.16265
|
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] |
https://github.com/yedizhang/linattn-icl
|
3,239
|
When and How Does CLIP Enable Domain and Compositional Generalization?
|
https://openreview.net/forum?id=Lktwi30g63
|
[
"Elias Kempf",
"Simon Schrodi",
"Max Argus",
"Thomas Brox"
] |
Spotlight
|
The remarkable generalization performance of contrastive vision-language models like CLIP is often attributed to the diversity of their training distributions. However, key questions remain unanswered: Can CLIP generalize to an entirely unseen domain when trained on a diverse mixture of domains (domain generalization)? Can it generalize to unseen classes within partially seen domains (compositional generalization)? What factors affect such generalization? To answer these questions, we trained CLIP models on systematically constructed training distributions with controlled domain diversity and object class exposure. Our experiments show that domain diversity is essential for both domain and compositional generalization, yet compositional generalization can be surprisingly weaker than domain generalization when the training distribution contains a suboptimal subset of the test domain. Through data-centric *and* mechanistic analyses, we find that successful generalization requires the learning of sufficiently shared representations in intermediate layers and circuits.
|
CLIP, Compositional Generalization, Domain Generalization, Out-of-Distribution Robustness, OOD generalization
|
We studied CLIP's domain and compositional generalization via systematic data-centric experiments and mechanistic analyses, revealing that domain diversity, sufficiently shared intermediate features and circuitry are crucial for generalization.
| 1,549
|
2502.09507
|
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] |
https://github.com/lmb-freiburg/understanding-clip-ood
|
3,240
|
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain
|
https://openreview.net/forum?id=Bm706VlAtU
|
[
"Gaozheng Pei",
"Ke Ma",
"Yingfei Sun",
"Qianqian Xu",
"Qingming Huang"
] |
Spotlight
|
The diffusion-based adversarial purification methods attempt to drown adversarial perturbations into a part of isotropic noise through the forward process, and then recover the clean images through the reverse process. Due to the lack of distribution information about adversarial perturbations in the pixel domain, it is often unavoidable to damage normal semantics. We turn to the frequency domain perspective, decomposing the image into amplitude spectrum and phase spectrum. We find that for both spectra, the damage caused by adversarial perturbations tends to increase monotonically with frequency. This means that we can extract the content and structural information of the original clean sample from the frequency components that are less damaged. Meanwhile, theoretical analysis indicates that existing purification methods indiscriminately damage all frequency components, leading to excessive damage to the image. Therefore, we propose a purification method that can eliminate adversarial perturbations while maximizing the preservation of the content and structure of the original image. Specifically, at each time step during the reverse process, for the amplitude spectrum, we replace the low-frequency components of the estimated image's amplitude spectrum with the corresponding parts of the adversarial image.
For the phase spectrum, we project the phase of the estimated image into a designated range of the adversarial image's phase spectrum, focusing on the low frequencies. Empirical evidence from extensive experiments demonstrates that our method significantly outperforms most current defense methods.
|
Adversarial Purification
|
Adversarial Purification from Perspective of Frequency Domain
| 1,502
|
2505.01267
|
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] |
https://github.com/GaozhengPei/FreqPure
|
3,241
|
Leveraging Diffusion Model as Pseudo-Anomalous Graph Generator for Graph-Level Anomaly Detection
|
https://openreview.net/forum?id=Zm2M92TZyO
|
[
"Jinyu Cai",
"Yunhe Zhang",
"Fusheng Liu",
"See-Kiong Ng"
] |
Spotlight
|
A fundamental challenge in graph-level anomaly detection (GLAD) is the scarcity of anomalous graph data, as the training dataset typically contains only normal graphs or very few anomalies. This imbalance hinders the development of robust detection models. In this paper, we propose **A**nomalous **G**raph **Diff**usion (AGDiff), a framework that explores the potential of diffusion models in generating pseudo-anomalous graphs for GLAD. Unlike existing diffusion-based methods that focus on modeling data normality, AGDiff leverages the latent diffusion framework to incorporate subtle perturbations into graph representations, thereby generating pseudo-anomalous graphs that closely resemble normal ones. By jointly training a classifier to distinguish these generated graph anomalies from normal graphs, AGDiff learns more discriminative decision boundaries. The shift from solely modeling normality to explicitly generating and learning from pseudo graph anomalies enables AGDiff to effectively identify complex anomalous patterns that other approaches might overlook. Comprehensive experimental results demonstrate that the proposed AGDiff significantly outperforms several state-of-the-art GLAD baselines.
|
Anomaly Detection, Diffusion Model, Graph Neural Network
| null | 1,490
| null |
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] | null |
3,242
|
An Analysis for Reasoning Bias of Language Models with Small Initialization
|
https://openreview.net/forum?id=4HQaMUYWAT
|
[
"Junjie Yao",
"Zhongwang Zhang",
"Zhi-Qin John Xu"
] |
Spotlight
|
Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks. We validate this reasoning bias via real datasets and meticulously designed anchor functions. Further analysis of initial training dynamics suggests that specific model components, particularly the embedding space and self-attention mechanisms, play pivotal roles in shaping these learning biases. We provide a theoretical framework from the perspective of model training dynamics to explain these phenomena. Additionally, experiments on real-world language tasks corroborate our theoretical insights. This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models.
|
initialization scale, reasoning bias, language model, embedding space, training dynamics
| null | 1,338
|
2502.04375
|
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] | null |
3,243
|
Instance Correlation Graph-based Naive Bayes
|
https://openreview.net/forum?id=hwTKGdM4TK
|
[
"Chengyuan Li",
"Liangxiao Jiang",
"Wenjun Zhang",
"Liangjun Yu",
"Huan Zhang"
] |
Spotlight
|
Due to its simplicity, effectiveness and robustness, naive Bayes (NB) has continued to be one of the top 10 data mining algorithms. To improve its performance, a large number of improved algorithms have been proposed in the last few decades. However, in addition to Gaussian naive Bayes (GNB), there is little work on numerical attributes. At the same time, none of them takes into account the correlations among instances. To fill this gap, we propose a novel algorithm called instance correlation graph-based naive Bayes (ICGNB). Specifically, it first uses original attributes to construct an instance correlation graph (ICG) to represent the correlations among instances. Then, it employs a variational graph auto-encoder (VGAE) to generate new attributes from the constructed ICG and uses them to augment original attributes.
Finally, it weights each augmented attribute to alleviate the attribute redundancy and builds GNB on the weighted attributes. The experimental results on tens of datasets show that ICGNB significantly outperforms its deserved competitors.Our codes and datasets are available at https://github.com/jiangliangxiao/ICGNB.
|
Naive Bayes, Numerical attribute, Instance correlation graph, Variational graph auto-encoder
|
A novel instance correlation graph-based naive Bayes (ICGNB) algorithm is proposed.
| 1,002
| null |
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] |
https://github.com/jiangliangxiao/ICGNB
|
3,244
|
Trusted Multi-View Classification with Expert Knowledge Constraints
|
https://openreview.net/forum?id=U64wEbM7NB
|
[
"Xinyan Liang",
"Shijie Wang",
"Yuhua Qian",
"Qian Guo",
"Liang Du",
"Bingbing Jiang",
"Tingjin Luo",
"Feijiang Li"
] |
Spotlight
|
Multi-view classification (MVC) based on the Dempster-Shafer theory has gained significant recognition for its reliability in safety-critical applications. However, existing methods predominantly focus on providing confidence levels for decision outcomes without explaining the reasoning behind these decisions. Moreover, the reliance on first-order statistical magnitudes of belief masses often inadequately capture the intrinsic uncertainty within the evidence. To address these limitations, we propose a novel framework termed Trusted Multi-view Classification Constrained with Expert Knowledge (TMCEK). TMCEK integrates expert knowledge to enhance feature-level interpretability and introduces a distribution-aware subjective opinion mechanism to derive more reliable and realistic confidence estimates. The theoretical superiority of the proposed uncertainty measure over conventional approaches is rigorously established. Extensive experiments conducted on three multi-view datasets for sleep stage classification demonstrate that TMCEK achieves state-of-the-art performance while offering interpretability at both the feature and decision levels. These results position TMCEK as a robust and interpretable solution for MVC in safety-critical domains. The code is available at https://github.com/jie019/TMCEK_ICML2025.
|
multi-view classification, trusted multi-view classification, trusted fusion, distribution-aware subjective opinion
| null | 850
| null |
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] |
https://github.com/jie019/TMCEK_ICML2025
|
3,245
|
Discrepancy Minimization in Input-Sparsity Time
|
https://openreview.net/forum?id=TmJvacopmV
|
[
"Yichuan Deng",
"Xiaoyu Li",
"Zhao Song",
"OMRI WEINSTEIN"
] |
Spotlight
|
A recent work by [Larsen, SODA 2023] introduced a faster combinatorial alternative to Bansal's SDP algorithm for finding a coloring $x \in \\{-1, 1\\}^n$ that approximately minimizes the discrepancy $\mathrm{disc}(A, x) := \\| A x \\|_{\infty}$ of a real-valued $m \times n$ matrix $A$. Larsen's algorithm runs in $\widetilde{O}(mn^2)$ time compared to Bansal's $\widetilde{O}(mn^{4.5})$-time algorithm, with a slightly weaker logarithmic approximation ratio in terms of the hereditary discrepancy of $A$ [Bansal, FOCS 2010]. We present a combinatorial $\widetilde{O}(\mathrm{nnz}(A) + n^3)$-time algorithm with the same approximation guarantee as Larsen's, optimal for tall matrices where $m = \mathrm{poly}(n)$. Using a more intricate analysis and fast matrix multiplication, we further achieve a runtime of $\widetilde{O}(\mathrm{nnz}(A) + n^{2.53})$, breaking the cubic barrier for square matrices and surpassing the limitations of linear-programming approaches [Eldan and Singh, RS\&A 2018]. Our algorithm relies on two key ideas: (i) a new sketching technique for finding a projection matrix with a short $\ell_2$-basis using implicit leverage-score sampling, and (ii) a data structure for efficiently implementing the iterative Edge-Walk partial-coloring algorithm [Lovett and Meka, SICOMP 2015], and using an alternative analysis to enable ``lazy'' batch updates with low-rank corrections. Our results nearly close the computational gap between real-valued and binary matrices, for which input-sparsity time coloring was recently obtained by [Jain, Sah and Sawhney, SODA 2023].
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combinatorial optimization, algorithmic discrepancy theory, sketching, input-sparsity time
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We give the algorithm for discrepancy minimization which runs in input-sparsity time.
| 816
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2210.12468
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] | null |
3,246
|
Sharp Generalization for Nonparametric Regression by Over-Parameterized Neural Networks: A Distribution-Free Analysis in Spherical Covariate
|
https://openreview.net/forum?id=fPOkujQBVb
|
[
"Yingzhen Yang"
] |
Spotlight
|
Sharp generalization bound for neural networks trained by gradient descent (GD) is of central interest in statistical learning theory and deep learning. In this paper, we consider nonparametric regression
by an over-parameterized two-layer NN trained by GD. We show that, if the neural network is trained by GD with early stopping, then the trained network renders a sharp rate of the nonparametric regression risk of $O(\epsilon_n^2)$, which is the same rate as that for the classical kernel regression trained by GD with early stopping, where $\epsilon_n$ is the critical population rate of the Neural Tangent Kernel (NTK) associated with the network and $n$ is the size of the training data. It is remarked that our result does not require distributional assumptions on the covariate as long as the covariate lies on the unit sphere, in a strong contrast with many existing results which rely on specific distributions such as the spherical uniform data distribution or distributions satisfying certain restrictive conditions.
As a special case of our general result, when the eigenvalues of the associated NTK
decay at a rate of $\lambda_j \asymp j^{-\frac{d}{d-1}}$ for $j \ge 1$ which happens under certain distributional assumption such as the training features follow the spherical uniform distribution, we immediately obtain the minimax optimal rate of
$O(n^{-\frac{d}{2d-1}})$, which is the major results of several existing works in this direction. The neural network width in our general result is lower bounded by a function of only $d$ and $\epsilon_n$, and such width does not depend on the minimum eigenvalue of the empirical NTK matrix whose lower bound usually requires additional assumptions on the training data.
Our results are built upon two significant technical results which are of independent interest. First, uniform convergence to the NTK is established during the training process by GD, so that we can have a nice decomposition of the neural network function at any step of the GD into a function in the Reproducing
Kernel Hilbert Space associated with the NTK and an error function with a small $L^{\infty}$-norm. Second, local Rademacher complexity is employed
to tightly bound the Rademacher complexity of the function class comprising all the possible neural network functions obtained by GD. Our result
formally fills the gap between training a classical kernel regression model and training an over-parameterized but finite-width neural network by GD for nonparametric regression without distributional assumptions about the spherical covariate.
|
Nonparametric Regression, Over-Parameterized Neural Network, Gradient Descent, Minimax Optimal Rate
|
We show that an over-parameterized two-layer neural network trained by gradient descent (GD) exhibits minimax optimal convergence rates for nonparametric regression, and our results are distribution-free in spherical covariate.
| 572
| null |
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] | null |
3,247
|
Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss
|
https://openreview.net/forum?id=S2K5MyRjrL
|
[
"Bo-Han Lai",
"Pin-Han Huang",
"Bo-Han Kung",
"Shang-Tse Chen"
] |
Spotlight
|
Lipschitz neural networks are well-known for providing certified robustness in deep learning. In this paper, we present a novel, efficient Block Reflector Orthogonal (BRO) layer that enhances the capability of orthogonal layers on constructing more expressive Lipschitz neural architectures. In addition, by theoretically analyzing the nature of Lipschitz neural networks, we introduce a new loss function that employs an annealing mechanism to increase margin for most data points. This enables Lipschitz models to provide better certified robustness. By employing our BRO layer and loss function, we design BRONet — a simple yet effective Lipschitz neural network that achieves state-of-the-art certified robustness. Extensive experiments and empirical analysis on CIFAR-10/100, Tiny-ImageNet, and ImageNet validate that our method outperforms existing baselines. The implementation is available at [GitHub Link](https://github.com/ntuaislab/BRONet).
|
Certified robustness, Adversarial
|
We propose a new orthogonal convolution and a novel loss function to enhance certified robustness.
| 559
|
2505.15174
|
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] |
https://github.com/ntuaislab/BRONet
|
3,248
|
Relational Invariant Learning for Robust Solvation Free Energy Prediction
|
https://openreview.net/forum?id=xVBfdltHST
|
[
"yeyunchen"
] |
Spotlight
|
Predicting the solvation free energy of molecules using graph neural networks holds significant potential for advancing drug discovery and the design of novel materials. While previous methods have demonstrated success on independent and identically distributed (IID) datasets, their performance in out-of-distribution (OOD) scenarios remains largely unexplored. We propose a novel Relational Invariant Learning framework (RILOOD) to enhance OOD generalization in solvation free energy prediction. RILOOD comprises three key components: (i) a mixup-based conditional modeling module that integrates diverse environments, (ii) a novel multi-granularity refinement strategy that extends beyond core substructures to enable context-aware representation learning for capturing multi-level interactions, and (iii) an invariant learning mechanism that identifies robust patterns generalizable to unseen environments. Extensive experiments demonstrate that RILOOD significantly outperforms state-of-the-art methods across various distribution shifts, highlighting its effectiveness in improving solvation free energy prediction under diverse conditions.
|
Molecule relational learning, graph neural network, out of distribution generalization
| null | 496
| null |
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] | null |
3,249
|
Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection
|
https://openreview.net/forum?id=GoGuB1yFko
|
[
"Xiang Fang",
"Arvind Easwaran",
"Blaise Genest"
] |
Spotlight
|
Out-of-distribution (OOD) detection attempts to distinguish outlier samples to prevent models trained on the in-distribution (ID) dataset from producing unavailable outputs. Most OOD detection methods require many ID samples for training, which seriously limits their real-world applications. To this end, we target a challenging setting: few-shot OOD detection, where only a few labeled ID samples are available. Therefore, few-shot OOD detection is much more challenging than the traditional OOD detection setting. Previous few-shot OOD detection works ignore the distinct diversity between different classes. In this paper, we propose a novel network: Adaptive Multi-prompt Contrastive Network (AMCN), which adapts the ID-OOD separation boundary by learning inter- and intra-class distribution. To compensate for the absence of OOD and scarcity of ID image samples, we leverage CLIP, connecting text with images, engineering learnable ID and OOD textual prompts. Specifically, we first generate adaptive prompts (learnable ID prompts, label-fixed OOD prompts, and label-adaptive OOD prompts). Then, we generate an adaptive class boundary for each class by introducing a class-wise threshold. Finally, we propose a prompt-guided ID-OOD separation module to control the margin between ID and OOD prompts. Experimental results show that AMCN outperforms other state-of-the-art works.
|
Adaptive Multi-prompt Contrastive Network
| null | 484
|
2506.17633
|
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] | null |
3,250
|
FlashTP: Fused, Sparsity-Aware Tensor Product for Machine Learning Interatomic Potentials
|
https://openreview.net/forum?id=wiQe95BPaB
|
[
"Seung Yul Lee",
"Hojoon Kim",
"Yutack Park",
"Dawoon Jeong",
"Seungwu Han",
"Yeonhong Park",
"Jae W. Lee"
] |
Spotlight
|
Machine Learning Interatomic Potentials (MLIPs) enable efficient molecular dynamics (MD) simulations with high accuracy. While equivariant MLIPs achieve state-of-the-art accuracy, they face significant computational bottlenecks centered around their Tensor-Product layer, which account for up to 75\% of training time and cause substantial memory overhead. We present FlashTP, a highly optimized tensor-product library that addresses these inefficiencies through kernel fusion, sparse computation, and path-aggregated execution. FlashTP achieves up to 41.6$\times$ and 60.8$\times$ kernel speedups over _e3nn_ and NVIDIA cuEquivariance, respectively. For SevenNet-l3i5, it delivers 4.2$\times$ and 3.5$\times$ speedup while reducing peak memory usage by 6.3$\times$ and 6.2$\times$ for inference and training, respectively. The code is available at https://github.com/SNU-ARC/flashTP.
|
Equivariant neural networks, Tensor Product, Software libraries, Efficiency, Machine-learned interatomic potential (MLIP), Machine Learning Force Fields (MLFF)
|
FlashTP accelerates equivariant MLIPs by optimizing Tensor-Product operations, achieving up to speedup and significantly reducing memory footprint.
| 450
| null |
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] |
https://github.com/SNU-ARC/flashTP
|
3,251
|
Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
|
https://openreview.net/forum?id=GazlTYxZss
|
[
"Shaokun Zhang",
"Ming Yin",
"Jieyu Zhang",
"Jiale Liu",
"Zhiguang Han",
"Jingyang Zhang",
"Beibin Li",
"Chi Wang",
"Huazheng Wang",
"Yiran Chen",
"Qingyun Wu"
] |
Spotlight
|
Failure attribution in LLM multi-agent systems—identifying the agent and step responsible for task failures—provides crucial clues for systems debugging but remains underexplored and labor-intensive.
In this paper, we propose and formulate a new research area: automated failure attribution for LLM multi-agent systems.
To support this initiative, we introduce the Who\&When dataset, comprising extensive failure logs from 127 LLM multi-agent systems with fine-grained annotations linking failures to specific agents and decisive error steps.
Using the Who\&When, we develop and evaluate three automated failure attribution methods, summarizing their corresponding pros and cons. The best method achieves 53.5\% accuracy in identifying failure-responsible agents but only 14.2\% in pinpointing failure steps, with some methods performing below random. Even SOTA reasoning models, such as OpenAI o1 and DeepSeek R1, fail to achieve practical usability. These results highlight the task's complexity and the need for further research in this area. Code and dataset are available in https://github.com/mingyin1/Agents_Failure_Attribution.
|
failure attribution, multi-agent systems.
| null | 425
|
2505.00212
|
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] |
https://github.com/mingyin1/Agents_Failure_Attribution
|
3,252
|
A Closer Look at Multimodal Representation Collapse
|
https://openreview.net/forum?id=Vf9f7eNX6T
|
[
"Abhra Chaudhuri",
"Anjan Dutta",
"Tu Bui",
"Serban Georgescu"
] |
Spotlight
|
We aim to develop a fundamental understanding of modality collapse, a recently observed empirical phenomenon wherein models trained for multimodal fusion tend to rely only on a subset of the modalities, ignoring the rest. We show that modality collapse happens when noisy features from one modality are entangled, via a shared set of neurons in the fusion head, with predictive features from another, effectively masking out positive contributions from the predictive features of the former modality and leading to its collapse. We further prove that cross-modal knowledge distillation implicitly disentangles such representations by freeing up rank bottlenecks in the student encoder, denoising the fusion-head outputs without negatively impacting the predictive features from either modality. Based on the above findings, we propose an algorithm that prevents modality collapse through explicit basis reallocation, with applications in dealing with missing modalities. Extensive experiments on multiple multimodal benchmarks validate our theoretical claims. Project page: https://abhrac.github.io/mmcollapse/.
|
Multimodal learning, modality collapse
|
Modality collapse happens as a result of cross-modal polysemantic entanglements arising out of rank bottlenecks in deep multimodal models, and can thus be remedied by freeing up such bottlenecks.
| 421
|
2505.22483
|
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] | null |
3,253
|
Geometric Hyena Networks for Large-scale Equivariant Learning
|
https://openreview.net/forum?id=jJRkkPr474
|
[
"Artem Moskalev",
"Mangal Prakash",
"Junjie Xu",
"Tianyu Cui",
"Rui Liao",
"Tommaso Mansi"
] |
Spotlight
|
Processing global geometric context while preserving equivariance is crucial when modeling biological, chemical, and physical systems. Yet, this is challenging due to the computational demands of equivariance and global context at scale. Standard methods such as equivariant self-attention suffer from quadratic complexity, while local methods such as distance-based message passing sacrifice global information. Inspired by the recent success of state-space and long-convolutional models, we introduce Geometric Hyena, the first equivariant long-convolutional model for geometric systems. Geometric Hyena captures global geometric context at sub-quadratic complexity while maintaining equivariance to rotations and translations. Evaluated on all-atom property prediction of large RNA molecules and full protein molecular dynamics, Geometric Hyena outperforms existing equivariant models while requiring significantly less memory and compute that equivariant self-attention. Notably, our model processes the geometric context of $30k$ tokens $20 \times$ faster than the equivariant transformer and allows $72 \times$ longer context within the same budget.
|
equivariance, global context, long convolution, scalability, mechanistic interpretability, architecrture
|
Geometric Hyena Networks is the first equivariant long-convolutional model that efficiently captures global geometric context at sub-quadratic complexity
| 339
|
2505.22560
|
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] | null |
3,254
|
Covered Forest: Fine-grained generalization analysis of graph neural networks
|
https://openreview.net/forum?id=xvLVYrYQ8a
|
[
"Antonis Vasileiou",
"Ben Finkelshtein",
"Floris Geerts",
"Ron Levie",
"Christopher Morris"
] |
Spotlight
|
The expressive power of message-passing graph neural networks (MPNNs) is reasonably well understood, primarily through combinatorial techniques from graph isomorphism testing. However, MPNNs' generalization abilities---making meaningful predictions beyond the training set---remain less explored. Current generalization analyses often overlook graph structure, limit the focus to specific aggregation functions, and assume the impractical, hard-to-optimize $0$-$1$ loss function. Here, we extend recent advances in graph similarity theory to assess the influence of graph structure, aggregation, and loss functions on MPNNs' generalization abilities. Our empirical study supports our theoretical insights, improving our understanding of MPNNs' generalization properties.
|
MPNNs, generalization, bounds, theory, Weisfeiler, Leman, Lehman
|
We provide tighter generalization bounds for MPNNs by considering the pseudometric geometry of MPNNs' feature space
| 252
|
2412.07106
|
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] |
https://github.com/benfinkelshtein/CoveredForests
|
3,255
|
Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning
|
https://openreview.net/forum?id=OpineZj5bj
|
[
"Shuai Yi",
"Yixiong Zou",
"Yuhua Li",
"Ruixuan Li"
] |
Spotlight
|
Vision Transformer (ViT) has achieved remarkable success due to its large-scale pretraining on general domains, but it still faces challenges when applying it to downstream distant domains that have only scarce training data, which gives rise to the Cross-Domain Few-Shot Learning (CDFSL) task. Inspired by Self-Attention's insensitivity to token orders, we find an interesting phenomenon neglected in current works: disrupting the continuity of image tokens (i.e., making pixels not smoothly transited across patches) in ViT leads to a noticeable performance decline in the general (source) domain but only a marginal decrease in downstream target domains. This questions the role of image tokens' continuity in ViT's generalization under large domain gaps. In this paper, we delve into this phenomenon for an interpretation. We find continuity aids ViT in learning larger spatial patterns, which are harder to transfer than smaller ones, enlarging domain distances. Meanwhile, it implies that only smaller patterns within each patch could be transferred under extreme domain gaps. Based on this interpretation, we further propose a simple yet effective method for CDFSL that better disrupts the continuity of image tokens, encouraging the model to rely less on large patterns and more on smaller ones. Extensive experiments show the effectiveness of our method in reducing domain gaps and outperforming state-of-the-art works. Codes and models are available at https://github.com/shuaiyi308/ReCIT.
|
Cross-Domain Few-Shot Learning
|
We find a phenomenon that disrupting image patches' continuity (e.g., shuffle patches) affects differently on source and target domains. We delve into it for an interpretation and propose a method based on it for CDFSL.
| 247
|
2506.03110
|
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] |
https://github.com/shuaiyi308/ReCIT
|
3,256
|
On the Tension between Byzantine Robustness and No-Attack Accuracy in Distributed Learning
|
https://openreview.net/forum?id=zU4VCPHYRC
|
[
"Yi-Rui Yang",
"Chang-Wei Shi",
"Wu-Jun Li"
] |
Spotlight
|
Byzantine-robust distributed learning (BRDL), which refers to distributed learning that can work with potential faulty or malicious workers (also known as Byzantine workers), has recently attracted much research attention. Robust aggregators are widely used in existing BRDL methods to obtain robustness against Byzantine workers. However, Byzantine workers do not always exist in applications. As far as we know, there is almost no existing work theoretically investigating the effect of using robust aggregators when there are no Byzantine workers. To bridge this knowledge gap, we theoretically analyze the aggregation error for robust aggregators when there are no Byzantine workers. Specifically, we show that the worst-case aggregation error without Byzantine workers increases with the increase of the number of Byzantine workers that a robust aggregator can tolerate. The theoretical result reveals the tension between Byzantine robustness and no-attack accuracy, which refers to accuracy without faulty workers and malicious workers in this paper. Furthermore, we provide lower bounds for the convergence rate of gradient descent with robust aggregators for non-convex objective functions and objective functions that satisfy the Polyak-Lojasiewicz (PL) condition, respectively. We also prove the tightness of the lower bounds. The lower bounds for convergence rate reveal similar tension between Byzantine robustness and no-attack accuracy. Empirical results further support our theoretical findings.
|
distributed machine learning, Byzantine robustness, robust aggregation
| null | 121
| null |
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] | null |
3,257
|
Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective
|
https://openreview.net/forum?id=otNB7BzsiR
|
[
"Weizhong Huang",
"Yuxin Zhang",
"Xiawu Zheng",
"Fei Chao",
"Rongrong Ji"
] |
Spotlight
|
In this paper, we address the challenge of determining the layer-wise sparsity rates of large language models (LLMs) through a theoretical perspective. Specifically, we identify a critical issue of **"reconstruction error explosion"** in existing LLMs sparsification methods. This refers to the cumulative effect of reconstruction errors throughout the sparsification process, where errors from earlier layers propagate and amplify in subsequent layers. As a result, the overall reconstruction error increases significantly, leading to a substantial degradation in model performance. Through theoretical analysis, we derive a simple yet effective approach to layer-wise sparsity allocation that mitigates this issue. Our method uses a monotonically increasing arithmetic progression, reducing the process of determining sparsity rates for multiple layers to the determination of a single common difference hyperparameter. Remarkably, this allows for the optimal layer-wise sparsity rates to be identified with just a few trials. Both our theoretical analysis and experimental results demonstrate that this sparsity allocation scheme is near optimal. Extensive experiments show that our method significantly improves the performance of sparse LLMs across various architectures, outperforming existing layer-wise sparsity methods. Furthermore, it enhances the performance of various compression techniques and is applicable to vision and multimodal models. Notably, our method achieves a reduction of 52.10 in perplexity for the 70% sparse LLaMA2-7B model obtained via Wanda, improves average zero-shot accuracy by 10.50%, and delivers speedups of 2.63$\times$ and 2.23$\times$ on CPU and GPU, respectively. Code is available at https://github.com/wzhuang-xmu/ATP.
|
Large language models, Network Sparsity, Layerwise sparsity
|
We derive the layer-wise sparsity rate of LLMs through a theoretical perspective, which significantly enhances the performance of sparse LLMs.
| 58
|
2502.14770
|
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] |
https://github.com/wzhuang-xmu/ATP
|
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