GenAI4ELab/papercli-papers huggingface dataset:
Novelty Detection in Reinforcement Learning with World Models
Authors: Geigh Zollicoffer; Kenneth Eaton; Jonathan C Balloch; Julia Kim; Wei Zhou; Robert Wright; Mark Riedl
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Reinforcement learning (RL) using world models has found significant recent successes.
However, when a sudden change to world mechanics or properties occurs then agent performance and reliability can dramatically decline.
We refer to the sudden change in visual properties or state transitions as novelties.
Implementing novelty detection within generated world model frameworks is a crucial
task for protecting the agent when deployed. In this paper, we propose straightforward bounding approaches to incorporate novelty detection into world model RL agents by utilizing the misalignment of the world model's hallucinated states and the true observed states as a novelty score.  
We provide
effective approaches to detecting novelties in a distribution of transitions learned by an agent in
a world model. Finally, we show the advantage of
our work in a novel environment compared to traditional machine learning novelty detection methods as well as currently accepted RL-focused novelty detection algorithms.
Compositional Risk Minimization
Authors: Divyat Mahajan; Mohammad Pezeshki; Charles Arnal; Ioannis Mitliagkas; Kartik Ahuja; Pascal Vincent
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Compositional generalization is a crucial step towards developing data-efficient intelligent machines that generalize in human-like ways. 
In this work, we tackle a challenging form of distribution shift, termed compositional shift, where some attribute combinations are completely absent at training but present in the test distribution. This shift tests the model's ability to generalize compositionally to novel attribute combinations in discriminative tasks. We model the data with flexible additive energy distributions, where each energy term represents an attribute, and derive a simple alternative to empirical risk minimization termed compositional risk minimization (CRM). We first train an additive energy classifier to predict the multiple attributes and then adjust this classifier to tackle compositional shifts. We provide an extensive theoretical analysis of CRM, where we show that our proposal extrapolates to special affine hulls of seen attribute combinations. Empirical evaluations on benchmark datasets confirms the improved robustness of CRM compared to other methods from the literature designed to tackle various forms of subpopulation shifts.
Automatically Interpreting Millions of Features in Large Language Models
Authors: Gonçalo Santos Paulo; Alex Troy Mallen; Caden Juang; Nora Belrose
Venue: ICML 2025 (ICML 2025 poster)

Abstract: While the activations of neurons in deep neural networks usually do not have a simple human-understandable interpretation, sparse autoencoders (SAEs) can be used to transform these activations into a higher-dimensional latent space which can be more easily interpretable. However, SAEs can have millions of distinct latents, making it infeasible for humans to manually interpret each one. In this work, we build an open-source automated pipeline to generate and evaluate natural language interpretations for SAE latents using LLMs. We test our framework on SAEs of varying sizes, activation functions, and losses, trained on two different open-weight LLMs. We introduce five new techniques to score the quality of interpretations that are cheaper to run than the previous state of the art. One of these techniques, intervention scoring, evaluates the interpretability of the effects of intervening on a latent, which we find explains latents that are not recalled by existing methods. We propose guidelines for generating better interpretations that remain valid for a broader set of activating contexts, and discuss pitfalls with existing scoring techniques. Our code is available at https://github.com/EleutherAI/delphi.
Learning Survival Distributions with the Asymmetric Laplace Distribution
Authors: Deming Sheng; Ricardo Henao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Probabilistic survival analysis models seek to estimate the distribution of the future occurrence (time) of an event given a set of covariates.
In recent years, these models have preferred nonparametric specifications that avoid directly estimating survival distributions via discretization.
Specifically, they estimate the probability of an individual event at fixed times or the time of an event at fixed probabilities (quantiles), using supervised learning.
Borrowing ideas from the quantile regression literature, we propose a parametric survival analysis method based on the Asymmetric Laplace Distribution (ALD).
This distribution allows for closed-form calculation of popular event summaries such as mean, median, mode, variation, and quantiles.
The model is optimized by maximum likelihood to learn, at the individual level, the parameters (location, scale, and asymmetry) of the ALD distribution.
Extensive results on synthetic and real-world data demonstrate that the proposed method outperforms parametric and nonparametric approaches in terms of accuracy, discrimination and calibration.
Sample Complexity of Branch-length Estimation by Maximum Likelihood
Authors: David Clancy Jr.; Hanbaek Lyu; Sebastien Roch
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We consider the branch-length estimation problem on a bifurcating tree: a character evolves along the edges of a binary tree according to a two-state symmetric Markov process, and we seek to recover the edge transition probabilities from repeated observations at the leaves. This problem arises in phylogenetics, and is related to latent tree graphical model inference. In general, the log-likelihood function is non-concave and may admit many critical points. Nevertheless, simple coordinate maximization has been known to perform well in practice, defying the complexity of the likelihood landscape. In this work, we provide the first theoretical guarantee as to why this might be the case. We show that deep inside the Kesten-Stigum reconstruction regime, provided with polynomially many $m$ samples (assuming the tree is balanced), there exists a universal parameter regime (independent of the size of the tree) where the log-likelihood function is strongly concave and smooth with high probability. On this high-probability likelihood landscape event, we show that the standard coordinate maximization algorithm converges exponentially fast to the maximum likelihood estimator, which is within $O(1/\sqrt{m})$ from the true parameter, provided a sufficiently close initial point.
What Do Learning Dynamics Reveal About Generalization in LLM Mathematical Reasoning?
Authors: Katie Kang; Amrith Setlur; Dibya Ghosh; Jacob Steinhardt; Claire Tomlin; Sergey Levine; Aviral Kumar
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Modern large language models (LLMs) excel at fitting finetuning data, but often struggle on unseen examples. In order to teach models genuine reasoning abilities rather than superficial pattern matching, our work aims to better understand how the learning dynamics of LLM finetuning shapes downstream generalization. Our analysis focuses on reasoning tasks, whose problem structure allows us to distinguish between memorization (the exact replication of reasoning steps from the training data) and performance (the correctness of the final solution). We find that a model's performance on test prompts can be effectively characterized by a training metric we call pre-memorization train accuracy: the accuracy of model samples on training queries before they begin to copy the exact reasoning steps from the training set. On the dataset level, this metric is able to almost perfectly predict test accuracy, achieving $R^2$ of $\geq 0.9$ across various models (Llama3 8B, Gemma2 9B), datasets (GSM8k, MATH), and training configurations. On a per-example level, this metric is also indicative of whether individual model predictions are robust to perturbations in the training query. By connecting a model's learning dynamics to test performance, pre-memorization train accuracy can inform training decisions, such as the makeup of the training data. Our experiments on data curation show that prioritizing examples with low pre-memorization accuracy leads to 1.5-2x improvements in data efficiency compared to i.i.d. data scaling and other data scaling techniques.
Understanding Generalization in Quantum Machine Learning with Margins
Authors: Tak Hur; Daniel K. Park
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Understanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current generalization theories, particularly those relying on uniform bounds, across both classical and quantum settings. In this work, we present a margin-based generalization bound for QML models, providing a more reliable framework for evaluating generalization. Our experimental studies on the quantum phase recognition dataset demonstrate that margin-based metrics are strong predictors of generalization performance, outperforming traditional metrics like parameter count. By connecting this margin-based metric to quantum information theory, we demonstrate how to enhance the generalization performance of QML through a classical-quantum hybrid approach when applied to classical data.
Best of Both Worlds: Advantages of Hybrid Graph Sequence Models
Authors: Ali Behrouz; Ali Parviz; Mahdi Karami; Clayton Sanford; Bryan Perozzi; Vahab Mirrokni
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Modern sequence models (e.g., Transformers and linear RNNs) emerged as dominant backbones of recent deep learning frameworks, mainly due to their efficiency, representational power, and/or ability to capture long-range dependencies. Recently, adopting these sequence models for graph-structured data has gained popularity as the alternative to Message Passing Neural Networks (MPNNs). There is, however, a lack of a common foundation about what constitutes a good graph sequence model, and a mathematical description of the benefits and deficiencies in adopting different sequence models for learning on graphs. To this end, we introduce the Graph Sequence Model (GSM), a unifying framework for applying sequence models to graph data.  The GSM framework allows us to understand, evaluate, and compare the power of different sequence model backbones in graph tasks. Building on this insight, we propose GSM++, a fast hybrid model that hierarchically tokenizes the graph using Hierarchical Affinity Clustering (HAC) and then encodes these sequences via a hybrid architecture. The  theoretical and experimental findings confirm that GSM++ outperforms baseline models on most benchmarks.
Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness
Authors: Konstantinos Oikonomidis; Jan Quan; Emanuel Laude; Panagiotis Patrinos
Venue: ICML 2025 (ICML 2025 oral)

Abstract: We analyze nonlinearly preconditioned gradient methods for solving smooth minimization problems. We introduce a generalized smoothness property, based on the notion of abstract convexity, that is broader than Lipschitz smoothness and provide sufficient first- and second-order conditions. Notably, our framework encapsulates algorithms associated with the gradient clipping method and brings out novel insights for the class of $(L_0,L_1)$-smooth functions that has received widespread interest recently, thus allowing us to extend beyond already established methods. We investigate the convergence of the proposed method in both the convex and nonconvex setting.
Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift
Authors: chao ying; Jun Jin; Yi Guo; Xiudi Li; Muxuan Liang; Jiwei Zhao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Collecting gold-standard phenotype data via manual extraction is typically labor-intensive and slow, whereas automated computational phenotypes (ACPs) offer a systematic and much faster alternative.
However, simply replacing the gold-standard with ACPs, without acknowledging their differences, could lead to biased results and misleading conclusions.
Motivated by the complexity of incorporating ACPs while maintaining the validity of downstream analyses, in this paper, we consider a semi-supervised learning setting that consists of both labeled data (with gold-standard) and unlabeled data (without gold-standard), under the covariate shift framework.
We develop doubly robust and semiparametrically efficient estimators that leverage ACPs for general target parameters in the unlabeled and combined populations. In addition,
we carefully analyze the efficiency gains achieved by incorporating ACPs, comparing scenarios with and without their inclusion.
Notably, we identify that ACPs for the unlabeled data, instead of for the labeled data, drive the enhanced efficiency gains. To validate our theoretical findings, we conduct comprehensive synthetic experiments and apply our method to multiple real-world datasets, confirming the practical advantages of our approach.
Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models
Authors: Anshuman Chhabra; Bo Li; Jian Chen; Prasant Mohapatra; Hongfu Liu
Venue: ICML 2025 (ICML 2025 oral)

Abstract: A core data-centric learning challenge is the identification of training samples that are detrimental to model performance. Influence functions serve as a prominent tool for this task and offer a robust framework for assessing training data influence on model predictions. Despite their widespread use, their high computational cost associated with calculating the inverse of the Hessian matrix pose constraints, particularly when analyzing large-sized deep models. In this paper, we establish a bridge between identifying detrimental training samples via influence functions and outlier gradient detection. This transformation not only presents a straightforward and Hessian-free formulation but also provides insights into the role of the gradient in sample impact. Through systematic empirical evaluations, we first validate the hypothesis of our proposed outlier gradient analysis approach on synthetic datasets. We then demonstrate its effectiveness in detecting mislabeled samples in vision models and selecting data samples for improving performance of natural language processing transformer models. We also extend its use to influential sample identification for fine-tuning Large Language Models.
MemFreezing: A Novel Adversarial Attack on Temporal Graph Neural Networks under Limited Future Knowledge
Authors: Yue Dai; Liang Liu; Xulong Tang; Youtao Zhang; Jun Yang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Temporal graph neural networks (TGNN) have achieved significant momentum in many real-world dynamic graph tasks.
While most existing TGNN attack methods assume worst-case scenarios where attackers have complete knowledge of the input graph, the assumption may not always hold in real-world situations, where attackers can, at best, access information about existing nodes and edges but not future ones after the attack.
However, studying adversarial attacks under these constraints is crucial, as limited future knowledge can reveal TGNN vulnerabilities overlooked in idealized settings.
Nevertheless, designing effective attacks in such scenarios is challenging: the evolving graph can weaken their impact and make it hard to affect unseen nodes. 
To address these challenges, we introduce MemFreezing, a novel adversarial attack framework that delivers long-lasting and spreading disruptions in TGNNs without requiring post-attack knowledge of the graph. 
MemFreezing strategically injects fake nodes or edges to push node memories into a stable “frozen state,” reducing their responsiveness to subsequent graph changes and limiting their ability to convey meaningful information. 
As the graph evolves, these affected nodes maintain and propagate their frozen state through their neighbors. 
Experimental results show that MemFreezing persistently degrades TGNN performance across various tasks, offering a more enduring adversarial strategy under limited future knowledge.
TopoTune: A Framework for Generalized Combinatorial Complex Neural Networks
Authors: Mathilde Papillon; Guillermo Bernardez; Claudio Battiloro; Nina Miolane
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graph Neural Networks (GNNs) effectively learn from relational data by leveraging graph symmetries. However, many real-world systems---such as biological or social networks---feature multi-way interactions that GNNs fail to capture. Topological Deep Learning (TDL) addresses this by modeling and leveraging higher-order structures, with Combinatorial Complex Neural Networks (CCNNs) offering a general and expressive approach that has been shown to outperform GNNs. However, TDL lacks the principled and standardized frameworks that underpin GNN development, restricting its accessibility and applicability. To address this issue, we introduce Generalized CCNNs (GCCNs), a simple yet powerful family of TDL models that can be used to systematically transform any (graph) neural network into its TDL counterpart. We prove that GCCNs generalize and subsume CCNNs, while extensive experiments on a diverse class of GCCNs show that these architectures consistently match or outperform CCNNs, often with less model complexity. In an effort to accelerate and democratize TDL, we introduce TopoTune, a lightweight software for defining, building, and training GCCNs with unprecedented flexibility and ease.
Self-Bootstrapping for Versatile Test-Time Adaptation
Authors: Shuaicheng Niu; Guohao Chen; Peilin Zhao; Tianyi Wang; Pengcheng Wu; Zhiqi Shen
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In this paper, we seek to develop a versatile test-time adaptation (TTA) objective for a variety of tasks — classification and regression across image-, object-, and pixel-level predictions. We achieve this through a self-bootstrapping scheme that optimizes prediction consistency between the test image (as target) and its deteriorated view. The key challenge lies in devising effective augmentations/deteriorations that: i) preserve the image’s geometric information, e.g., object sizes and locations, which is crucial for TTA on object/pixel-level tasks, and ii) provide sufficient learning signals for TTA. To this end, we analyze how common distribution shifts affect the image's information power across spatial frequencies in the Fourier domain, and reveal that low-frequency components carry high power and masking these components supplies more learning signals, while masking high-frequency components can not. In light of this, we randomly mask the low-frequency amplitude of an image in its Fourier domain for augmentation. Meanwhile, we also augment the image with noise injection to compensate for missing learning signals at high frequencies, by enhancing the information power there. Experiments show that, either independently or as a plug-and-play module, our method achieves superior results across classification, segmentation, and 3D monocular detection tasks with both transformer and CNN models.
R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Authors: Harsh Goel; Mohammad Omama; Behdad Chalaki; Vaishnav Tadiparthi; Ehsan Moradi Pari; Sandeep P. Chinchali
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Multi-agent reinforcement learning (MARL) has achieved significant progress in large-scale traffic control, autonomous vehicles, and robotics. Drawing inspiration from biological systems where roles naturally emerge to enable coordination, role-based MARL methods have been proposed to enhance cooperation learning for complex tasks. However, existing methods exclusively derive roles from an agent's past experience during training, neglecting their influence on its future trajectories. This paper introduces a key insight: an agent’s role should shape its future behavior to enable effective coordination. Hence, we propose Role Discovery and Diversity through Dynamics Models (R3DM), a novel role-based MARL framework that learns emergent roles by maximizing the mutual information between agents' roles, observed trajectories, and expected future behaviors. R3DM optimizes the proposed objective through contrastive learning on past trajectories to first derive intermediate roles that shape intrinsic rewards to promote diversity in future behaviors across different roles through a learned dynamics model. Benchmarking on SMAC and SMACv2 environments demonstrates that R3DM outperforms state-of-the-art MARL approaches, improving multi-agent coordination to increase win rates by up to 20%. The code is available at https://github.com/UTAustin-SwarmLab/R3DM.
Proto Successor Measure: Representing the Behavior Space of an RL Agent
Authors: Siddhant Agarwal; Harshit Sikchi; Peter Stone; Amy Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment without additional interactions. Referred to as "zero-shot learning", this ability remains elusive for general-purpose reinforcement learning algorithms.  While recent works have attempted to produce zero-shot RL agents, they make assumptions about the nature of the tasks or the structure of the MDP. We present *Proto Successor Measure*: the basis set for all possible behaviors of a Reinforcement Learning Agent in a dynamical system. We prove that any possible behavior (represented using visitation distributions) can be represented using an affine combination of these policy-independent basis functions. Given a reward function at test time, we simply need to find the right set of linear weights to combine these bases corresponding to the optimal policy. We derive a practical algorithm to learn these basis functions using reward-free interaction data from the environment and show that our approach can produce the near-optimal policy at test time for any given reward function without additional environmental interactions. Project page: agarwalsiddhant10.github.io/projects/psm.html.
From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control
Authors: Peiyan Hu; Xiaowei Qian; Wenhao Deng; Rui Wang; Haodong Feng; Ruiqi Feng; Tao Zhang; Long Wei; Yue Wang; Zhi-Ming Ma; Tailin Wu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The application of deep learning for partial differential equation (PDE)-constrained control is gaining increasing attention. However, existing methods rarely consider safety requirements crucial in real-world applications. To address this limitation, we propose Safe Diffusion Models for PDE Control (SafeDiffCon), which introduce the uncertainty quantile as model uncertainty quantification to achieve optimal control under safety constraints through both post-training and inference phases. Firstly, our approach post-trains a pre-trained diffusion model to generate control sequences that better satisfy safety constraints while achieving improved control objectives via a reweighted diffusion loss, which incorporates the uncertainty quantile estimated using conformal prediction. Secondly, during inference, the diffusion model dynamically adjusts both its generation process and parameters through iterative guidance and fine-tuning, conditioned on control targets while simultaneously integrating the estimated uncertainty quantile. We evaluate SafeDiffCon on three control tasks: 1D Burgers' equation, 2D incompressible fluid, and controlled nuclear fusion problem. Results demonstrate that SafeDiffCon is the only method that satisfies all safety constraints, whereas other classical and deep learning baselines fail. Furthermore, while adhering to safety constraints, SafeDiffCon achieves the best control performance. The code can be found at https://github.com/AI4Science-WestlakeU/safediffcon.