GenAI4ELab/papercli-papers huggingface dataset:
CoDy: Counterfactual Explainers for Dynamic Graphs
Authors: Zhan Qu; Daniel Gomm; Michael Färber
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Temporal Graph Neural Networks (TGNNs) are widely used to model dynamic systems where relationships and features evolve over time. Although TGNNs demonstrate strong predictive capabilities in these domains, their complex architectures pose significant challenges for explainability. Counterfactual explanation methods provide a promising solution by illustrating how modifications to input graphs can influence model predictions. To address this challenge, we present CoDy—Counterfactual Explainer for Dynamic Graphs—a model-agnostic, instance-level explanation approach that identifies counterfactual subgraphs to interpret TGNN predictions. CoDy employs a search algorithm that combines Monte Carlo Tree Search with heuristic selection policies, efficiently exploring a vast search space of potential explanatory subgraphs by leveraging spatial, temporal, and local event impact information. Extensive experiments against state-of-the-art factual and counterfactual baselines demonstrate CoDy's effectiveness, with improvements of 16% in AUFSC+ over the strongest baseline. Our code is available at: https://github.com/daniel-gomm/CoDy
Hi-Patch: Hierarchical Patch GNN for Irregular Multivariate Time Series
Authors: Yicheng Luo; Bowen Zhang; Zhen Liu; Qianli Ma
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Multi-scale information is crucial for multivariate time series modeling. However, most existing time series multi-scale analysis methods treat all variables in the same manner, making them unsuitable for Irregular Multivariate Time Series (IMTS), where variables have distinct origin scales/sampling rates. To fill this gap, we propose Hi-Patch, a hierarchical patch graph network. Hi-Patch encodes each observation as a node, represents and captures local temporal and inter-variable dependencies of densely sampled variables through an intra-patch graph layer, and obtains patch-level nodes through aggregation. These nodes are then updated and re-aggregated through a stack of inter-patch graph layers, where several scale-specific graph networks progressively extract more global temporal and inter-variable features of both sparsely and densely sampled variables under specific scales. The output of the last layer is fed into task-specific decoders to adapt to different downstream tasks. Experiments on 8 datasets demonstrate that Hi-Patch outperforms state-of-the-art models in IMTS forecasting and classification tasks.
DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence
Authors: Yang Jiao; Kai Yang; Chengtao Jian
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Trilevel learning (TLL) with zeroth order constraints is a fundamental problem in machine learning, arising in scenarios where gradient information is inaccessible due to data privacy or model opacity, such as in federated learning, healthcare, and financial systems. These problems are notoriously difficult to solve due to their inherent complexity and the lack of first order information. Moreover, in many practical scenarios, data may be distributed across various nodes, necessitating strategies to address trilevel learning problems without centralizing data on servers to uphold data privacy. To this end, an effective distributed trilevel zeroth order learning framework DTZO is proposed in this work to address the trilevel learning problems with level-wise zeroth order constraints in a distributed manner. The proposed DTZO is versatile and can be adapted to a wide range of (grey-box) trilevel learning problems with partial zeroth order constraints. In DTZO, the cascaded polynomial approximation can be constructed without relying on gradients or sub-gradients, leveraging a novel cut, i.e., zeroth order cut. Furthermore, we theoretically carry out the non-asymptotic convergence rate analysis for the proposed DTZO in achieving the $\epsilon$-stationary point. Extensive experiments have been conducted to demonstrate and validate the superior performance of the proposed DTZO.
Revisiting the Predictability of Performative, Social Events
Authors: Juan Carlos Perdomo
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Social predictions do not passively describe the future; they actively shape it. They inform actions and change individual expectations in ways that influence the likelihood of the predicted outcome. Given these dynamics, to what extent can social events be predicted? This question was discussed throughout the 20th century by authors like Merton, Morgenstern, Simon, and others who considered it a central issue in social science methodology. In this work, we provide a modern answer to this old problem. Using recent ideas from performative prediction and outcome indistinguishability, we establish that one can always efficiently predict social events accurately, regardless of how predictions influence data. While achievable, we also show that these predictions are often undesirable, highlighting the limitations of previous desiderata. We end with a discussion of various avenues forward.
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
Authors: Yao Shu; Wenyang Hu; See-Kiong Ng; Bryan Kian Hsiang Low; Fei Yu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large Language Models (LLMs) have become indispensable in numerous real-world applications. However, fine-tuning these models at scale, especially in federated settings where data privacy and communication efficiency are critical, presents significant challenges. Existing approaches often resort to parameter-efficient fine-tuning (PEFT) to mitigate communication overhead, but this typically comes at the cost of model accuracy. To this end, we propose *federated full-parameter tuning at scale for LLMs* (Ferret), **the first first-order method with shared randomness** to enable scalable full-parameter tuning of LLMs across decentralized data sources while maintaining competitive model accuracy. Ferret accomplishes this through three aspects: **(i)** it employs widely used first-order methods for efficient local updates; **(ii)** it projects these updates into a low-dimensional space to considerably reduce communication overhead; and **(iii)** it reconstructs local updates from this low-dimensional space with shared randomness to facilitate effective full-parameter global aggregation, ensuring fast convergence and competitive final performance. Our rigorous theoretical analyses and insights along with extensive experiments, show that Ferret significantly enhances the scalability of existing federated full-parameter tuning approaches by achieving high computational efficiency, reduced communication overhead, and fast convergence, all while maintaining competitive model accuracy. Our implementation is available at [https://github.com/allen4747/Ferret](https://github.com/allen4747/Ferret).
A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization
Authors: Bokun Wang; Tianbao Yang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper studies a class of convex Finite-sum Coupled Compositional Optimization (cFCCO) problems with applications including group distributionally robust optimization (GDRO) and learning with imbalanced data. To better address these problems, we introduce an efficient single-loop primal-dual block-coordinate stochastic algorithm called ALEXR. The algorithm employs block-coordinate stochastic mirror ascent with extrapolation for the dual variable and stochastic proximal gradient descent updates for the primal variable. We establish the convergence rates of ALEXR in both convex and strongly convex cases under smoothness and non-smoothness conditions of involved functions, which not only improve the best rates in previous works on smooth cFCCO problems but also expand the realm of cFCCO for solving more challenging non-smooth problems such as the dual form of GDRO.  Finally, we derive lower complexity bounds, demonstrating the (near-)optimality of ALEXR within a broad class of stochastic algorithms for cFCCO. Experimental results on GDRO and partial Area Under the ROC Curve (pAUC) maximization demonstrate the promising performance of our algorithm.
Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development
Authors: Daoyuan Chen; Haibin Wang; Yilun Huang; Ce Ge; Yaliang Li; Bolin Ding; Jingren Zhou
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: The emergence of multimodal large models has advanced artificial intelligence, introducing unprecedented levels of performance and functionality. However, optimizing these models remains challenging due to historically isolated paths of model-centric and data-centric developments, leading to suboptimal outcomes and inefficient resource utilization. In response, we present a new sandbox suite tailored for integrated data-model co-development. This sandbox provides a feedback-driven experimental platform, enabling cost-effective iteration and guided refinement of both data and models. Our proposed ``Probe-Analyze-Refine'' workflow, validated through practical use cases on multimodal tasks such as image-text pre-training with CLIP, image-to-text generation with LLaVA-like models, and text-to-video generation with DiT-based models, yields transferable and notable performance boosts, such as topping the VBench leaderboard. A comprehensive set of over 100 experiments demonstrated the suite's usability and extensibility, while also uncovering insights into the interplay between data quality, diversity, model behavior, and computational costs. All codes, datasets, and models are open-sourced to foster future research and applications that would otherwise be infeasible due to the lack of a dedicated co-development infrastructure.
RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers
Authors: Xuwei Xu; Yang Li; Yudong Chen; Jiajun Liu; Sen Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We reveal that feedforward network (FFN) layers, rather than attention layers, are the primary contributors to Vision Transformer (ViT) inference latency, with their impact signifying as model size increases. This finding highlights a critical opportunity for optimizing the efficiency of large-scale ViTs by focusing on FFN layers. In this work, we propose a novel channel idle mechanism that facilitates post-training structural reparameterization for efficient FFN layers during testing. Specifically, a set of feature channels remains idle and bypasses the nonlinear activation function in each FFN layer, thereby forming a linear pathway that enables structural reparameterization during inference. This mechanism results in a family of **RePa**rameterizable **Vi**sion **T**ransformers (RePaViTs), which achieve remarkable latency reductions with acceptable sacrifices (sometimes gains) in accuracy across various ViTs. The benefits of our method scale consistently with model sizes, demonstrating greater speed improvements and progressively narrowing accuracy gaps or even higher accuracies on larger models. In particular, RePa-ViT-Large and RePa-ViT-Huge enjoy **66.8%** and **68.7%** speed-ups with **+1.7%** and **+1.1%** higher top-1 accuracies under the same training strategy, respectively. RePaViT is the first to employ structural reparameterization on FFN layers to expedite ViTs to our best knowledge, and we believe that it represents an auspicious direction for efficient ViTs. Source code is available at https://github.com/Ackesnal/RePaViT.
LAST SToP for Modeling Asynchronous Time Series
Authors: Shubham Gupta; Thibaut Durand; Graham W. Taylor; Lilian Bialokozowicz
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present a novel prompt design for Large Language Models (LLMs) tailored to **Asynchronous Time Series**. Unlike regular time series, which assume values at evenly spaced time points, asynchronous time series consist of timestamped events occurring at irregular intervals, each described in natural language. Our approach effectively utilizes the rich natural language of event descriptions, allowing LLMs to benefit from their broad world knowledge for reasoning across different domains and tasks. This allows us to extend the scope of asynchronous time series analysis beyond forecasting to include tasks like anomaly detection and data imputation. We further introduce **Stochastic Soft Prompting**, a novel prompt-tuning mechanism that significantly improves model performance, outperforming existing finetuning methods such as QLORA. Through extensive experiments on real-world datasets, we demonstrate that our approach achieves state-of-the-art performance across different tasks and datasets.
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle
Authors: Hui Dai; Ryan Teehan; Mengye Ren
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Many existing evaluation benchmarks for Large Language Models (LLMs) quickly become outdated due to the emergence of new models and training data. These benchmarks also fall short in assessing how LLM performance changes over time, as they consist of a static set of questions without a temporal dimension. To address these limitations, we propose using future event prediction as a continuous evaluation method to assess LLMs' temporal generalization and forecasting abilities. Our benchmark, Daily Oracle, automatically generates question-answer (QA) pairs from daily news, challenging LLMs to predict "future" event outcomes. Our findings reveal that as pre-training data becomes outdated, LLM performance degrades over time. While Retrieval Augmented Generation (RAG) has the potential to enhance prediction accuracy, the performance degradation pattern persists, highlighting the need for continuous model updates. Code and data are available at https://agenticlearning.ai/daily-oracle.
Retraining-free Merging of Sparse MoE via Hierarchical Clustering
Authors: I-Chun Chen; Hsu-Shen Liu; Wei-Fang Sun; Chen-Hao Chao; Yen-Chang Hsu; Chun-Yi Lee
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Sparse Mixture-of-Experts (SMoE) models represent a significant advancement in large language model (LLM) development through their efficient parameter utilization. These models achieve substantial performance improvements at reduced
inference costs. However, the deployment of SMoE models faces constraints from extensive memory requirements of expert components in resource-limited environments. To address these limitations, this paper introduces Hierarchical Clustering for Sparsely activated Mixture of Experts (HC-SMoE), a task-agnostic expert merging framework for parameter reduction without retraining. HC-SMoE introduces a novel hierarchical clustering approach based on expert outputs to ensure merging robustness independent of routing decisions. The proposed output-based clustering method enables effective capture of functional relationships between experts for large-
scale architectures. We provide theoretical analysis and comprehensive evaluations across multiple zero-shot language tasks to demonstrate HC-SMoE’s effectiveness in state-of-the-art models including Qwen and Mixtral. The experimental results validate HC-SMoE’s superior performance and practical applicability for real-world deployments. Our implementation is available at https://github.com/wazenmai/HC-SMoE.
Lightweight-Mark: Rethinking Deep Learning-Based Watermarking
Authors: Yupeng Qiu; Han Fang; Ee-Chien Chang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Deep learning-based watermarking models play a crucial role in copyright protection across various applications. However, many high-performance models are limited in practical deployment due to their large number of parameters. Meanwhile, the robustness and invisibility performance of existing lightweight models are unsatisfactory. This presents a pressing need for a watermarking model that combines lightweight capacity with satisfactory performance. Our research identifies a key reason that limits the performance of existing watermarking frameworks: a mismatch between commonly used decoding losses (e.g., mean squared error and binary cross-entropy loss) and the actual decoding goal, leading to parameter redundancy. We propose two innovative solutions: (1) Decoding-oriented surrogate loss (DO), which redesigns the loss function to mitigate the influence of decoding-irrelevant optimization directions; and (2) Detachable projection head (PH), which incorporates a detachable redundant module during training to handle these irrelevant directions and is discarded during inference. Additionally, we propose a novel watermarking framework comprising five submodules, allowing for independent parameter reduction in each component. Our proposed model achieves better efficiency, invisibility, and robustness while utilizing only 2.2\% of the parameters compared to the state-of-the-art frameworks. By improving efficiency while maintaining robust copyright protection, our model is well suited for practical applications in resource-constrained environments. The DO and PH methods are designed to be plug-and-play, facilitating seamless integration into future lightweight models.
Refining Adaptive Zeroth-Order Optimization at Ease
Authors: Yao Shu; Qixin Zhang; Kun He; Zhongxiang Dai
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recently, zeroth-order (ZO) optimization plays an essential role in scenarios where gradient information is inaccessible or unaffordable, such as black-box systems and resource-constrained environments. While existing adaptive methods such as ZO-AdaMM have shown promise, they are fundamentally limited by their underutilization of moment information during optimization, usually resulting in underperforming convergence. To overcome these limitations, this paper introduces *Refined Adaptive Zeroth-Order Optimization* (R-AdaZO). Specifically, we first show the untapped variance reduction effect of first moment estimate on ZO gradient estimation, which improves the accuracy and stability of ZO updates. We then refine the second moment estimate based on these variance-reduced gradient estimates to better capture the geometry of the optimization landscape, enabling a more effective scaling of ZO updates. We present rigorous theoretical analysis to show **_(a)_** *the first analysis* to the variance reduction of first moment estimate in ZO optimization, **_(b)_** *the improved second moment estimates* with a more accurate approximation of its variance-free ideal, **_(c)_** *the first variance-aware convergence framework* for adaptive ZO methods, which may be of independent interest, and **_(d)_** *the faster convergence* of R-AdaZO than existing baselines like ZO-AdaMM. Our extensive experiments, including synthetic problems, black-box adversarial attack, and memory-efficient fine-tuning of large language models (LLMs), further verify the superior convergence of R-AdaZO, indicating that R-AdaZO offers an improved solution for real-world ZO optimization challenges.
GenMol: A Drug Discovery Generalist with Discrete Diffusion
Authors: Seul Lee; Karsten Kreis; Srimukh Prasad Veccham; Meng Liu; Danny Reidenbach; Yuxing Peng; Saee Gopal Paliwal; Weili Nie; Arash Vahdat
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Drug discovery is a complex process that involves multiple stages and tasks. However, existing molecular generative models can only tackle some of these tasks. We present *Generalist Molecular generative model* (GenMol), a versatile framework that uses only a *single* discrete diffusion model to handle diverse drug discovery scenarios. GenMol generates Sequential Attachment-based Fragment Embedding (SAFE) sequences through non-autoregressive bidirectional parallel decoding, thereby allowing the utilization of a molecular context that does not rely on the specific token ordering while having better sampling efficiency. GenMol uses fragments as basic building blocks for molecules and introduces *fragment remasking*, a strategy that optimizes molecules by regenerating masked fragments, enabling effective exploration of chemical space. We further propose *molecular context guidance* (MCG), a guidance method tailored for masked discrete diffusion of GenMol. GenMol significantly outperforms the previous GPT-based model in *de novo* generation and fragment-constrained generation, and achieves state-of-the-art performance in goal-directed hit generation and lead optimization. These results demonstrate that GenMol can tackle a wide range of drug discovery tasks, providing a unified and versatile approach for molecular design.
Simple Policy Optimization
Authors: Zhengpeng Xie; Qiang Zhang; Fan Yang; Marco Hutter; Renjing Xu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Model-free reinforcement learning algorithms have seen remarkable progress, but key challenges remain. Trust Region Policy Optimization (TRPO) is known for ensuring monotonic policy improvement through conservative updates within a trust region, backed by strong theoretical guarantees. However, its reliance on complex second-order optimization limits its practical efficiency. Proximal Policy Optimization (PPO) addresses this by simplifying TRPO's approach using ratio clipping, improving efficiency but sacrificing some theoretical robustness. This raises a natural question: Can we combine the strengths of both methods? In this paper, we introduce Simple Policy Optimization (SPO), a novel unconstrained first-order algorithm. By slightly modifying the policy loss used in PPO, SPO can achieve the best of both worlds. Our new objective improves upon ratio clipping, offering stronger theoretical properties and better constraining the probability ratio within the trust region. Empirical results demonstrate that SPO outperforms PPO with a simple implementation, particularly for training large, complex network architectures end-to-end.
Deep Bayesian Filter for Bayes-Faithful Data Assimilation
Authors: Yuta Tarumi; Keisuke Fukuda; Shin-ichi Maeda
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Data assimilation for nonlinear state space models (SSMs) is inherently challenging due to non-Gaussian posteriors. We propose Deep Bayesian Filtering (DBF), a novel approach to data assimilation in nonlinear SSMs. DBF introduces latent variables $h_t$ in addition to physical variables $z_t$, ensuring Gaussian posteriors by (i) constraining state transitions in the latent space to be linear and (ii) learning a Gaussian inverse observation operator $r(h_t|o_t)$. This structured posterior design enables analytical recursive computation, avoiding the accumulation of Monte Carlo sampling errors over time steps. DBF optimizes these operators and other latent SSM parameters by maximizing the evidence lower bound. Experiments demonstrate that DBF outperforms existing methods in scenarios with highly non-Gaussian posteriors.