GenAI4ELab/papercli-papers huggingface dataset:
Fusing Reward and Dueling Feedback in Stochastic Bandits
Authors: Xuchuang Wang; Qirun Zeng; Jinhang Zuo; Xutong Liu; Mohammad Hajiesmaili; John C.S. Lui; Adam Wierman
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper investigates the fusion of absolute (reward) and relative (dueling) feedback in stochastic bandits,
    where both feedback types are gathered in each decision round.
    We derive a regret lower bound, demonstrating that an efficient algorithm may incur only the smaller among the reward and dueling-based regret for each individual arm.
    We propose two fusion approaches:
    (1) a simple elimination fusion algorithm that leverages both feedback types to explore all arms and unifies collected information by sharing a common candidate arm set,
    and (2) a decomposition fusion algorithm that selects the more effective feedback to explore the corresponding arms
    and
    randomly assigns one feedback type for exploration and the other for exploitation in each round.
    The elimination fusion experiences a suboptimal multiplicative term of the number of arms in regret due to the intrinsic suboptimality of dueling elimination.
    In contrast, the decomposition fusion achieves regret matching the lower bound up to a constant under a common assumption.
    Extensive experiments confirm the efficacy of our algorithms and theoretical results.
Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger
Authors: Qi Yang; Chenghao Zhang; Lubin Fan; Kun Ding; Jieping Ye; Shiming Xiang
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: 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.
Efficient Distributed Optimization under Heavy-Tailed Noise
Authors: Su Hyeong Lee; Manzil Zaheer; Tian Li
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Distributed optimization has become the default training paradigm in modern machine learning due to the growing scale of models and datasets. To mitigate communication overhead, local updates are often applied before global aggregation, resulting in a nested optimization approach with inner and outer steps. However, heavy-tailed stochastic gradient noise remains a significant challenge, particularly in attention-based models, hindering effective training. In this work, we propose TailOPT, an efficient framework designed to address heavy-tailed noise by leveraging adaptive optimization and novel clipping techniques. We establish convergence guarantees for the TailOPT framework under heavy-tailed noise with local updates and potentially unbounded gradient variance. 
Among its variants, we propose a memory- and communication-efficient instantiation (named $Bi^2Clip$) that performs coordinate-wise clipping from both above and below at both the inner and outer optimizers. $Bi^2Clip$ brings about benefits of adaptive optimization (e.g., Adam) without the cost of maintaining or transmitting additional gradient statistics. Empirically, TailOPT, including $Bi^2Clip$, demonstrates superior performance on various tasks and models compared with state-of-the-art methods, while being more efficient.
Rethink the Role of Deep Learning towards Large-scale Quantum Systems
Authors: Yusheng Zhao; Chi Zhang; Yuxuan Du
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Characterizing the ground state properties of quantum systems is fundamental to capturing their behavior but computationally challenging. Recent advances in AI have introduced novel approaches, with diverse machine learning (ML) and deep learning (DL) models proposed for this purpose. However, the necessity and specific role of DL models in these tasks remain unclear, as prior studies often employ varied or impractical quantum resources to construct datasets, resulting in unfair comparisons. To address this, we systematically benchmark DL models against traditional ML approaches across three families of Hamiltonian, scaling up to $127$ qubits in three crucial ground-state learning tasks while enforcing equivalent quantum resource usage. Our results reveal that ML models often achieve performance comparable to or even exceeding that of DL approaches across all tasks. Furthermore, a randomization test demonstrates that measurement input features have minimal impact on DL models' prediction performance. These findings challenge the necessity of current DL models in many quantum system learning scenarios and provide valuable insights into their effective utilization.
Continual Generalized Category Discovery: Learning and Forgetting from a Bayesian Perspective
Authors: Hao Dai; Jagmohan Chauhan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Continual Generalized Category Discovery (C-GCD) faces a critical challenge: incrementally learning new classes from unlabeled data streams while preserving knowledge of old classes. 
    Existing methods struggle with catastrophic forgetting, especially when unlabeled data mixes known and novel categories. 
    We address this by analyzing C-GCD’s forgetting dynamics through a Bayesian lens, revealing that covariance misalignment between old and new classes drives performance degradation. 
    Building on this insight, we propose Variational Bayes C-GCD (VB-CGCD), a novel framework that integrates variational inference with covariance-aware nearest-class-mean classification. 
    VB-CGCD adaptively aligns class distributions while suppressing pseudo-label noise via stochastic variational updates. 
    Experiments show VB-CGCD surpasses prior art by +15.21% with the overall accuracy in the final session on standard benchmarks. 
    We also introduce a new challenging benchmark with only 10% labeled data and extended online phases—VB-CGCD achieves a 67.86% final accuracy, significantly higher than state-of-the-art (38.55%), demonstrating its robust applicability across diverse scenarios. Code is available at: https://github.com/daihao42/VB-CGCD
Improving Out-of-Distribution Detection with Markov Logic Networks
Authors: Konstantin Kirchheim; Frank Ortmeier
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Out-of-distribution (OOD) detection is essential for ensuring the reliability of deep learning models operating in open-world scenarios. Current OOD detectors mainly rely on statistical models to identify unusual patterns in the latent representations of a deep neural network. This work proposes to augment existing OOD detectors with probabilistic reasoning, utilizing Markov logic networks (MLNs). MLNs connect first-order logic with probabilistic reasoning to assign probabilities to inputs based on weighted logical constraints defined over human-understandable concepts, which offers improved explainability. Through extensive experiments on multiple datasets, we demonstrate that MLNs can significantly enhance the performance of a wide range of existing OOD detectors while maintaining computational efficiency. Furthermore, we introduce a simple algorithm for learning logical constraints for OOD detection from a dataset and showcase its effectiveness.
Toward Efficient Kernel-Based Solvers for Nonlinear PDEs
Authors: Zhitong Xu; Da Long; Yiming Xu; Guang Yang; Shandian Zhe; Houman Owhadi
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce a novel kernel learning framework toward efficiently solving nonlinear partial differential equations (PDEs). In contrast to the state-of-the-art kernel solver that embeds differential operators within kernels, posing challenges with a large number of collocation points, our approach eliminates these operators from the kernel. We model the solution using a standard kernel interpolation form and differentiate the interpolant to compute the derivatives. Our framework obviates the need for complex Gram matrix construction between solutions and their derivatives, allowing for a straightforward implementation and scalable computation. As an instance, we allocate the collocation points on a grid and adopt a product kernel, which yields a Kronecker product structure in the interpolation. This structure enables us to avoid computing the full Gram matrix, reducing costs and scaling efficiently to a large number of collocation points. We provide a proof of the convergence and rate analysis of our method under appropriate regularity assumptions. In numerical experiments, we demonstrate the advantages of our method in solving several benchmark PDEs.
Fully Heteroscedastic Count Regression with Deep Double Poisson Networks
Authors: Spencer Young; Porter Jenkins; Longchao Da; Jeff Dotson; Hua Wei
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Neural networks capable of accurate, input-conditional uncertainty representation are essential for real-world AI systems. Deep ensembles of Gaussian networks have proven highly effective for continuous regression due to their ability to flexibly represent aleatoric uncertainty via unrestricted heteroscedastic variance, which in turn enables accurate epistemic uncertainty estimation. However, no analogous approach exists for $\textit{count}$ regression, despite many important applications. To address this gap, we propose the Deep Double Poisson Network (DDPN), a novel neural discrete count regression model that outputs the parameters of the Double Poisson distribution, enabling arbitrarily high or low predictive aleatoric uncertainty for count data and improving epistemic uncertainty estimation when ensembled. We formalize and prove that DDPN exhibits robust regression properties similar to heteroscedastic Gaussian models via learnable loss attenuation, and introduce a simple loss modification to control this behavior. Experiments on diverse datasets demonstrate that DDPN outperforms current baselines in accuracy, calibration, and out-of-distribution detection, establishing a new state-of-the-art in deep count regression.
Neural Collapse Beyond the Unconstrained Features Model:  Landscape, Dynamics, and Generalization in the Mean-Field Regime
Authors: Diyuan Wu; Marco Mondelli
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: 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.
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Authors: Erpai Luo; Xinran Wei; Lin Huang; Yunyang Li; Han Yang; Zaishuo Xia; Zun Wang; Chang Liu; Bin Shao; Jia Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph neural networks have achieved remarkable success in this domain, their substantial computational cost—driven by high-order tensor product (TP) operations—restricts their scalability to large molecular systems with extensive basis sets. To address this challenge, we introduce **SPH**Net, an efficient and scalable equivariant network, that incorporates adaptive **SP**arsity into **H**amiltonian prediction. SPHNet employs two innovative sparse gates to selectively constrain non-critical interaction combinations, significantly reducing tensor product computations while maintaining accuracy. To optimize the sparse representation, we develop a Three-phase Sparsity Scheduler, ensuring stable convergence and achieving high performance at sparsity rates of up to 70\%. Extensive evaluations on QH9 and PubchemQH datasets demonstrate that SPHNet achieves state-of-the-art accuracy while providing up to a 7x speedup over existing models. Beyond Hamiltonian prediction, the proposed sparsification techniques also hold significant potential for improving the efficiency and scalability of other SE(3) equivariant networks, further broadening their applicability and impact.
Robust and Conjugate Spatio-Temporal Gaussian Processes
Authors: William Laplante; Matias Altamirano; Andrew B. Duncan; Jeremias Knoblauch; Francois-Xavier Briol
Venue: ICML 2025 (ICML 2025 poster)

Abstract: State-space formulations allow for Gaussian process (GP) regression with linear-in-time computational cost in spatio-temporal settings, but performance typically suffers in the presence of outliers. In this paper, we adapt and specialise the *robust and conjugate GP (RCGP)* framework of Altamirano et al. (2024) to the spatio-temporal setting. In doing so, we obtain an outlier-robust spatio-temporal GP with a computational cost comparable to classical spatio-temporal GPs. We also overcome the three main drawbacks of RCGPs: their unreliable performance when the prior mean is chosen poorly, their lack of reliable uncertainty quantification, and the need to carefully select a hyperparameter by hand. We study our method extensively in finance and weather forecasting applications, demonstrating that it provides a reliable approach to spatio-temporal modelling in the presence of outliers.
From Thousands to Billions: 3D Visual Language Grounding via Render-Supervised Distillation from 2D VLMs
Authors: Ang Cao; Sergio Arnaud; Oleksandr Maksymets; Jianing Yang; Ayush Jain; Ada Martin; Vincent-Pierre Berges; Paul McVay; Ruslan Partsey; Aravind Rajeswaran; Franziska Meier; Justin Johnson; Jeong Joon Park; Alexander Sax
Venue: ICML 2025 (ICML 2025 poster)

Abstract: 3D vision-language grounding faces a fundamental data bottleneck: while 2D models train on billions of images, 3D models have access to only thousands of labeled scenes--a six-order-of-magnitude gap that severely limits performance. We introduce \textbf{\emph{LIFT-GS}}, a practical distillation technique that overcomes this limitation by using differentiable rendering to bridge 3D and 2D supervision. LIFT-GS predicts 3D Gaussian representations from point clouds and uses them to render predicted language-conditioned 3D masks into 2D views, enabling supervision from 2D foundation models (SAM, CLIP, LLaMA) without requiring any 3D annotations. This render-supervised formulation enables end-to-end training of complete encoder-decoder architectures and is inherently model-agnostic.  LIFT-GS achieves state-of-the-art results with 25.7\% mAP on open-vocabulary instance segmentation (vs. 20.2\% prior SOTA) and consistent 10-30\% improvements on referential grounding tasks. Remarkably, pretraining effectively multiplies fine-tuning datasets by 2×, demonstrating strong scaling properties that suggest 3D VLG currently operates in a severely data-scarce regime. Project page: \url{https://liftgs.github.io}.
Strategic A/B testing via Maximum Probability-driven Two-armed Bandit
Authors: Yu Zhang; Shanshan Zhao; Bokui Wan; Jinjuan Wang; Xiaodong Yan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Detecting a minor average treatment effect is a major challenge in large-scale applications, where even minimal improvements can have a significant economic impact. Traditional methods, reliant on normal distribution-based or expanded statistics, often fail to identify such minor effects because of their inability to handle small discrepancies with sufficient sensitivity. This work leverages a counterfactual outcome framework and proposes a maximum probability-driven two-armed bandit (TAB) process by weighting the mean volatility statistic, which controls Type I error. The implementation of permutation methods further enhances the robustness and efficacy. The established strategic central limit theorem (SCLT) demonstrates that our approach yields a more concentrated distribution under the null hypothesis and a less concentrated one under the alternative hypothesis, greatly improving statistical power. The experimental results indicate a significant improvement in the A/B testing, highlighting the potential to reduce experimental costs while maintaining high statistical power.
Detecting Strategic Deception with Linear Probes
Authors: Nicholas Goldowsky-Dill; Bilal Chughtai; Stefan Heimersheim; Marius Hobbhahn
Venue: ICML 2025 (ICML 2025 poster)

Abstract: AI models might use deceptive strategies as part of scheming or misaligned behaviour. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal reasoning is misaligned. We thus evaluate if linear probes can robustly detect deception by monitoring model activations. We test two probe-training datasets, one with contrasting instructions to be honest or deceptive (following Zou et al. (2023)) and one of responses to simple roleplaying scenarios. We test whether these probes generalize to realistic settings where Llama-3.3-70B-Instruct behaves deceptively, such as concealing insider trading Scheurer et al. (2023) and purposely underperforming on safety evaluations Benton et al. (2024). We find that our probe distinguishes honest and deceptive responses with AUROCs between 0.96 and 0.999 on our evaluation datasets. If we set the decision threshold to have a 1\% false positive rate on chat data not related to deception, our probe catches 95-99\% of the deceptive responses. Overall we think white-box probes are promising for future monitoring systems, but current performance is insufficient as a robust defence against deception. Our probes' outputs can be viewed at https://data.apolloresearch.ai/dd/ and our code at https://github.com/ApolloResearch/deception-detection.
Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits
Authors: Jinzhao Li; Nan Jiang; Yexiang Xue
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Satisfiability Modulo Counting (SMC) is a recently proposed general language to reason about problems integrating statistical and symbolic Artificial Intelligence. 
An SMC problem is an extended SAT problem in which the truth values of a few Boolean variables are determined by probabilistic inference. Approximate solvers may return solutions that violate constraints. Directly integrating available SAT solvers and probabilistic inference solvers gives exact solutions but results in slow performance because of many back-and-forth invocations of both solvers. 
We propose KOCO-SMC, an integrated exact SMC solver that efficiently tracks lower and upper bounds in the probabilistic inference process. It enhances computational efficiency by enabling early estimation of probabilistic inference using only partial variable assignments, whereas existing methods require full variable assignments. 
In the experiment, we compare KOCO-SMC with currently available approximate and exact SMC solvers on large-scale datasets and real-world applications. The proposed KOCO-SMC finds exact solutions with much less time.
The Empirical Mean is Minimax Optimal for Local Glivenko-Cantelli
Authors: Doron Cohen; Aryeh Kontorovich; Roi Weiss
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We revisit the recently introduced Local Glivenko-Cantelli setting, which studies distribution-dependent uniform convergence rates of the Empirical Mean Estimator (EME). In this work, we investigate generalizations of this setting where arbitrary estimators are allowed rather than just the EME. Can a strictly larger class of measures be learned? Can better risk decay rates be obtained? We provide exhaustive answers to these questions—which are both negative, provided the learner is barred from exploiting some infinite-dimensional pathologies. On the other hand, allowing such exploits does lead to a strictly larger class of learnable measures.
Conformal Anomaly Detection in Event Sequences
Authors: Shuai Zhang; Chuan Zhou; Yang Liu; Peng Zhang; Xixun Lin; Shirui Pan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Anomaly detection in continuous-time event sequences is a crucial task in safety-critical applications. While existing methods primarily focus on developing a superior test statistic, they fail to provide guarantees regarding the false positive rate (FPR), which undermines their reliability in practical deployments. In this paper, we propose CADES (Conformal Anomaly Detection in Event Sequences), a novel test procedure based on conformal inference for the studied task with finite-sample FPR control. Specifically, by using the time-rescaling theorem, we design two powerful non-conformity scores tailored to event sequences, which exhibit complementary sensitivities to different abnormal patterns. CADES combines these scores with Bonferroni correction to leverage their respective strengths and addresses non-identifiability issues of existing methods. Theoretically, we prove the validity of CADES and further provide strong guarantees on calibration-conditional FPR control. Experimental results on synthetic and real-world datasets, covering various types of anomalies, demonstrate that CADES outperforms state-of-the-art methods while maintaining FPR control.