GenAI4ELab/papercli-papers huggingface dataset:
Policy Optimization for CMDPs with Bandit Feedback: Learning Stochastic and Adversarial Constraints
Authors: Francesco Emanuele Stradi; Anna Lunghi; Matteo Castiglioni; Alberto Marchesi; Nicola Gatti
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We study online learning in constrained Markov decision processes (CMDPs) in which rewards and constraints may be either stochastic or adversarial. In such settings, stradi et al. (2024) proposed the first best-of-both-worlds algorithm able to seamlessly handle stochastic and adversarial constraints, achieving optimal regret and constraint violation bounds in both cases. This algorithm suffers from two major drawbacks. First, it only works under full feedback, which severely limits its applicability in practice. Moreover, it relies on optimizing over the space of occupancy measures, which requires solving convex optimization problems, an highly inefficient task. In this paper, we provide the first best-of-both-worlds algorithm for CMDPs with bandit feedback. Specifically, when the constraints are stochastic, the algorithm achieves $\widetilde{\mathcal{O}}(\sqrt{T})$ regret and constraint violation, while, when they are adversarial, it attains $\widetilde{\mathcal{O}}(\sqrt{T})$ constraint violation and a tight fraction of the optimal reward. Moreover, our algorithm is based on a policy optimization approach, which is much more efficient than occupancy-measure-based methods.
Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning
Authors: Le-Trung Nguyen; Aël Quélennec; Van-Tam Nguyen; Enzo Tartaglione
Venue: ICML 2025 (ICML 2025 poster)

Abstract: On-device learning has emerged as a promising direction for AI development, particularly because of its potential to reduce latency issues and mitigate privacy risks associated with device-server communication, while improving energy efficiency. Despite these advantages, significant memory and computational constraints still represent major challenges for its deployment. Drawing on previous studies on low-rank decomposition methods that address activation memory bottlenecks in backpropagation, we propose a novel shortcut approach as an alternative. Our analysis and experiments demonstrate that our method can reduce activation memory usage, even up to $120.09\times$ compared to vanilla training, while also reducing overall training FLOPs up to $1.86\times$ when evaluated on traditional benchmarks.
Transfer Q-Learning with Composite MDP Structures
Authors: Jinhang Chai; Elynn Chen; Lin Yang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: To bridge the gap between empirical success and theoretical understanding in transfer reinforcement learning (RL), we study a principled approach with provable performance guarantees. We introduce a novel composite MDP framework where high-dimensional transition dynamics are modeled as the sum of a low-rank component representing shared structure and a sparse component capturing task-specific variations. This relaxes the common assumption of purely low-rank transition models, allowing for more realistic scenarios where tasks share core dynamics but maintain individual variations. We introduce UCB-TQL (Upper Confidence Bound Transfer Q-Learning), designed for transfer RL scenarios where multiple tasks share core linear MDP dynamics but diverge along sparse dimensions. When applying UCB-TQL to a target task after training on a source task with sufficient trajectories, we achieve a regret bound of $\tilde{\mathcal{O}}(\sqrt{eH^5N})$ that scales independently of the ambient dimension. Here, $N$ represents the number of trajectories in the target task, while $e$ quantifies the sparse differences between tasks. This result demonstrates substantial improvement over single task RL by effectively leveraging their structural similarities. Our theoretical analysis provides rigorous guarantees for how UCB-TQL simultaneously exploits shared dynamics while adapting to task-specific variations.
Implicit degree bias in the link prediction task
Authors: Rachith Aiyappa; Xin Wang; Munjung Kim; Ozgur Can Seckin; Yong-Yeol Ahn; Sadamori Kojaku
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Link prediction---a task of distinguishing actual hidden edges from random unconnected node pairs---is one of the quintessential tasks in graph machine learning. Despite being widely accepted as a universal benchmark and a downstream task for representation learning, the link prediction benchmark's validity has rarely been questioned. Here, we show that the common edge sampling procedure in the link prediction task has an implicit bias toward high-degree nodes. This produces a highly skewed evaluation that favors methods overly dependent on node degree. In fact  a ``null'' link prediction method based solely on node degree can yield nearly optimal performance in this setting. We propose a degree-corrected link prediction benchmark that offers a more reasonable assessment and better aligns with the performance on the recommendation task. Finally, we demonstrate that the degree-corrected benchmark can more effectively train graph machine-learning models by reducing overfitting to node degrees and facilitating the learning of relevant structures in graphs.
Layer-wise Quantization for Quantized Optimistic Dual Averaging
Authors: Anh Duc Nguyen; Ilia Markov; Frank Zhengqing Wu; Ali Ramezani-Kebrya; Kimon Antonakopoulos; Dan Alistarh; Volkan Cevher
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Modern deep neural networks exhibit heterogeneity across  numerous layers of various types such as residuals, multi-head attention, etc., due to varying structures (dimensions, activation functions, etc.), distinct representation characteristics, which impact predictions. We develop a general layer-wise quantization framework with tight variance and code-length bounds, adapting to the heterogeneities over the course of training. We then apply a new layer-wise quantization technique within distributed variational inequalities (VIs), proposing a novel Quantized Optimistic Dual Averaging (QODA) algorithm with adaptive learning rates, which achieves competitive convergence rates for monotone VIs. We empirically show that QODA achieves up to a $150$% speedup over the baselines in end-to-end training time for training Wasserstein GAN on $12+$ GPUs.
Joint Metric Space Embedding by Unbalanced Optimal Transport with Gromov–Wasserstein Marginal Penalization
Authors: Florian Beier; Moritz Piening; Robert Beinert; Gabriele Steidl
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We propose a new approach for unsupervised alignment of heterogeneous datasets, which maps data from two different domains without any known correspondences to a common metric space. Our method is based on an unbalanced optimal transport problem with Gromov-Wasserstein marginal penalization. It can be seen as a counterpart to the recently introduced joint multidimensional scaling method. We prove that there exists a minimizer of our functional and that for penalization parameters going to infinity, the corresponding sequence of minimizers converges to a minimizer of the so-called embedded Wasserstein distance. Our model can be reformulated as a quadratic, multi-marginal, unbalanced optimal transport problem, for which a bi-convex relaxation admits a numerical solver via block-coordinate descent. We provide numerical examples for joint embeddings in Euclidean as well as non-Euclidean spaces.
Dataflow-Guided Neuro-Symbolic Language Models for Type Inference
Authors: Gen Li; Yao Wan; Hongyu Zhang; Zhou Zhao; Wenbin Jiang; Xuanhua Shi; Hai Jin; Zheng Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Language Models (LMs) are increasingly used for type inference, aiding in error detection and software development. 
Some real-world deployments of LMs require the model to run on local machines to safeguard the intellectual property of the source code. This setting often limits the size of the LMs that can be used. We present Nester, the first neuro-symbolic approach that enhances LMs for type inference by integrating symbolic learning without increasing model size. Nester breaks type inference into sub-tasks based on the data and control flow of the input code, encoding them as a modular high-level program. This program executes multi-step actions, such as evaluating expressions and analyzing conditional branches of the target code, combining static typing with LMs to infer potential types. 
Evaluated on the ManyTypes4Py dataset in Python, Nester outperforms two state-of-the-art type inference methods (HiTyper and TypeGen), achieving 70.7\% Top-1 Exact Match, which is 18.3\% and 3.6\% higher than HiTyper and TypeGen, respectively. For complex type annotations like typing.Optional and typing.Union, Nester achieves 51.0\% and 16.7\%, surpassing TypeGen by 28.3\% and 5.8\%.
Tool Unlearning for Tool-Augmented LLMs
Authors: Jiali Cheng; Hadi Amiri
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Tool-augmented large language models (LLMs) may need to forget learned tools due to security concerns, privacy restrictions, or deprecated tools. However, ``tool unlearning'' has not been investigated in machine unlearning literature. We introduce this novel task, which requires addressing distinct challenges compared to traditional unlearning: knowledge removal rather than forgetting individual samples, the high cost of optimizing LLMs, and the need for principled evaluation metrics. To bridge these gaps, we propose ToolDelete , the first approach for unlearning tools from tool-augmented LLMs which implements three properties for effective tool unlearning, and a new membership inference attack (MIA) model for evaluation. Experiments on three tool learning datasets and tool-augmented LLMs show that ToolDelete effectively unlearns both randomly selected and category-specific tools, while preserving the LLM's knowledge on non-deleted tools and maintaining performance on general tasks.
Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms
Authors: Christian Komusiewicz; Andre Schidler; Frank Sommer; Manuel Sorge; Luca Pascal Staus
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Binary decision diagrams (BDDs) are widely applied tools to compactly represent labeled data as directed acyclic graphs; for efficiency and interpretability reasons small BDDs are preferred.
Given labeled data, minimizing BDDs is NP-complete and thus recent research focused on the influence of parameters such as the solution size $s$ on the complexity [Ordyniak et al., AAAI 2024].
Our main positive result is an algorithm that is efficient if in particular $s$, the domain size $D$, and the Hamming distance between any two data points is small, improving on previous running-time bounds.
This algorithm is inspired by the witness-tree paradigm that was recently successful for computing decision trees [Komusiewicz et al., ICML 2023], whose extension to BDDs was open.
We extend our algorithmic results to the case where we allow a small number of misclassified data points and complement them with lower bounds that show that the running times are tight from multiple points of view.
We show that our main algorithm holds practical promise by providing a proof-of-concept implementation.
Otter: Generating Tests from Issues to Validate SWE Patches
Authors: Toufique Ahmed; Jatin Ganhotra; Rangeet Pan; Avraham Shinnar; Saurabh Sinha; Martin Hirzel
Venue: ICML 2025 (ICML 2025 poster)

Abstract: While there has been plenty of work on generating tests from existing code, there has been limited work on generating tests from issues. A correct test must validate the code patch that resolves the issue. This paper focuses on the scenario where that code patch does not yet exist. Doing so supports two major use-cases. First, it supports TDD (test-driven development), the discipline of "test first, write code later" that has well-documented benefits for human software engineers. Second, it also validates SWE (software engineering) agents, which generate code patches for resolving issues. This paper introduces TDD-Bench-Verified, a benchmark for generating tests from issues, and Otter, an LLM-based solution for this task. Otter augments LLMs with rule-based analysis to check and repair their outputs, and introduces a novel self-reflective action planner. Experiments show Otter outperforming state-of-the-art systems for generating tests from issues, in addition to enhancing systems that generate patches from issues. We hope that Otter helps make developers more productive at resolving issues and leads to more robust, well-tested code.
Maximizing Intermediate Checkpoint Value in LLM Pretraining with Bayesian Optimization
Authors: Deyuan Liu; Zecheng Wang; Bingning Wang; Weipeng Chen; Chunshan Li; Zhiying Tu; Dianhui Chu; Dianbo Sui
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The rapid proliferation of large language models (LLMs), such as GPT-4 and Gemini, underscores the intense demand for resources during their training processes, posing significant challenges due to substantial computational and environmental costs. In this paper, we introduce a novel checkpoint merging strategy aimed at making efficient use of intermediate checkpoints during LLM pretraining. This method utilizes intermediate checkpoints with shared training trajectories, and is rooted in an extensive search space exploration for the best merging weight via Bayesian optimization. Through various experiments, we demonstrate that: (1) Our proposed methodology exhibits the capacity to augment pretraining, presenting an opportunity akin to obtaining substantial benefits at minimal cost; (2) Our proposed methodology, despite requiring a given held-out dataset, still demonstrates robust generalization capabilities across diverse domains, a pivotal aspect in pretraining.
Survival Analysis via Density Estimation
Authors: Hiroki Yanagisawa; Shunta Akiyama
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper introduces a novel framework for survival analysis by reinterpreting it as a form of density estimation. Our algorithm post-processes density estimation outputs to derive survival functions, enabling the application of any density estimation model to effectively estimate survival functions. This approach broadens the toolkit for survival analysis and enhances the flexibility and applicability of existing techniques. Our framework is versatile enough to handle various survival analysis scenarios, including competing risk models for multiple event types. It can also address dependent censoring when prior knowledge of the dependency between event time and censoring time is available in the form of a copula. In the absence of such information, our framework can estimate the upper and lower bounds of survival functions, accounting for the associated uncertainty.
Equivariant Neural Tangent Kernels
Authors: Philipp Misof; Pan Kessel; Jan E Gerken
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Little is known about the training dynamics of equivariant neural networks, in particular how it compares to data augmented training of their non-equivariant counterparts. Recently, neural tangent kernels (NTKs) have emerged as a powerful tool to analytically study the training dynamics of wide neural networks. In this work, we take an important step towards a theoretical understanding of training dynamics of equivariant models by deriving neural tangent kernels for a broad class of equivariant architectures based on group convolutions. As a demonstration of the capabilities of our framework, we show an interesting relationship between data augmentation and group convolutional networks. Specifically, we prove that they share the same expected prediction over initializations at all training times and even off the data manifold. In this sense, they have the same training dynamics. We demonstrate in numerical experiments that this still holds approximately for finite-width ensembles. By implementing equivariant NTKs for roto-translations in the plane ($G=C_{n}\ltimes\mathbb{R}^{2}$) and 3d rotations ($G=\mathrm{SO}(3)$), we show that equivariant NTKs outperform their non-equivariant counterparts as kernel predictors for histological image classification and quantum mechanical property prediction.
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model
Authors: Kaiwen Tang; Zhanglu Yan; Weng-Fai Wong
Venue: ICML 2025 (ICML 2025 poster)

Abstract: For reasons such as privacy, there are use cases for language models at the edge. This has given rise to small language models targeted for deployment in resource-constrained devices where energy efficiency is critical. Spiking neural networks (SNNs) offer a promising solution due to their energy efficiency, and there are already works on realizing transformer-based models on SNNs. However, key operations like softmax and layer normalization (LN) are difficult to implement on neuromorphic hardware, and many of these early works sidestepped them. To address these challenges, we introduce Sorbet, a transformer-based spiking language model that is more neuromorphic hardware-compatible. Sorbet incorporates a novel shifting-based softmax called PTsoftmax and a BitShifting-based PowerNorm (BSPN), both designed to replace the respective energy-intensive operations. By leveraging knowledge distillation and model quantization, Sorbet achieved a highly compressed binary weight model that maintains competitive performance while achieving $27.16\times$ energy savings compared to BERT. We validate Sorbet through extensive testing on the GLUE benchmark and a series of ablation studies, demonstrating its potential as an energy-efficient solution for language model inference.  Our code is publicly available at [https://github.com/Kaiwen-Tang/Sorbet](https://github.com/Kaiwen-Tang/Sorbet)
Learning Time-Varying Multi-Region Brain Communications  via Scalable Markovian Gaussian Processes
Authors: Weihan Li; Yule Wang; Chengrui Li; Anqi Wu
Venue: ICML 2025 (ICML 2025 oral)

Abstract: Understanding and constructing brain communications that capture dynamic communications across multiple regions is fundamental to modern system neuroscience, yet current methods struggle to find time-varying region-level communications or scale to large neural datasets with long recording durations. We present a novel framework using Markovian Gaussian Processes to learn brain communications with time-varying temporal delays from multi-region neural recordings, named Adaptive Delay Model (ADM). Our method combines Gaussian Processes with State Space Models and employs parallel scan inference algorithms, enabling efficient scaling to large datasets while identifying concurrent  communication patterns that evolve over time. This time-varying approach captures how brain region interactions shift dynamically during cognitive processes. Validated on synthetic and multi-region neural recordings datasets, our approach discovers both the directionality and temporal dynamics of neural communication. This work advances our understanding of distributed neural computation and provides a scalable tool for analyzing dynamic brain networks.
Automated Benchmark Generation for Repository-Level Coding Tasks
Authors: Konstantinos Vergopoulos; Mark Niklas Mueller; Martin Vechev
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Code Agent development is an extremely active research area, where a reliable performance metric is critical for tracking progress and guiding new developments. This demand is underscored by the meteoric rise in popularity of SWE-Bench -- a benchmark that challenges code agents to generate patches addressing GitHub issues given the full repository as context. The correctness of generated patches is then evaluated by executing a human-written test suite extracted from the repository after the issue's resolution.

However, constructing benchmarks like SWE-Bench requires substantial manual effort to set up historically accurate execution environments for testing. Crucially, this severely limits the number of considered repositories, e.g., just 12 for SWE-Bench. Considering so few repositories, selected for their popularity runs the risk of leading to a distributional mismatch, i.e., the measured performance may not be representative of real-world scenarios running the riks of misguiding development efforts. 

In this work, we address this challenge and introduce SetUpAgent, a fully automated system capable of historically accurate dependency setup, test execution, and result parsing. Using SetUpAgent, we generate two new datasets: (i) SWEE-Bench an extended version of SWE-Bench encompassing hundreds of repositories, and (ii) SWA-Bench a benchmark focusing on applications rather than libraries. Comparing these datasets to SWE-Bench with respect to their characteristics and code agent performance, we find significant distributional differences, including lower issue description quality and detail level, higher fix complexity, and most importantly up to 60% lower agent success rates.
GCAL: Adapting Graph Models to Evolving Domain Shifts
Authors: Ziyue Qiao; Qianyi Cai; Hao Dong; Jiawei Gu; Pengyang Wang; Meng Xiao; Xiao Luo; Hui Xiong
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper addresses the challenge of graph domain adaptation on evolving, multiple out-of-distribution (OOD) graphs.
Conventional graph domain adaptation methods are confined to single-step adaptation, making them ineffective in handling continuous domain shifts and prone to catastrophic forgetting. This paper introduces the Graph Continual Adaptive Learning (GCAL) method, designed to enhance model sustainability and adaptability across various graph domains. GCAL employs a bilevel optimization strategy. The "adapt" phase uses an information maximization approach to fine-tune the model with new graph domains while re-adapting past memories to mitigate forgetting. Concurrently, the "generate memory" phase, guided by a theoretical lower bound derived from information bottleneck theory, involves a variational memory graph generation module to condense original graphs into memories. Extensive experimental evaluations demonstrate that GCAL substantially outperforms existing methods in terms of adaptability and knowledge retention.
Unnatural Languages Are Not Bugs but Features for LLMs
Authors: Keyu Duan; Yiran Zhao; Zhili Feng; Jinjie Ni; Tianyu Pang; Qian Liu; Tianle Cai; Longxu Dou; Kenji Kawaguchi; Anirudh Goyal; J Zico Kolter; Michael Qizhe Shieh
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large Language Models (LLMs) have been observed to process non-human-readable text sequences, such as jailbreak prompts, often viewed as a bug for aligned LLMs. In this work, we present a systematic investigation challenging this perception, demonstrating that unnatural languages - strings that appear incomprehensible to humans but maintain semantic meanings for LLMs - contain latent features usable by models. Notably, unnatural languages possess latent features that can be generalized across different models and tasks during inference. Furthermore, models fine-tuned on unnatural versions of instruction datasets perform on-par with those trained on natural language, achieving \(49.71\) win rates in Length-controlled AlpacaEval 2.0 in average across various base models. In addition, through comprehensive analysis,  we demonstrate that LLMs process unnatural languages by filtering noise and inferring contextual meaning from filtered words. Our code is publicly available at https://github.com/John-AI-Lab/Unnatural_Language.