GenAI4ELab/papercli-papers huggingface dataset:
Subobject-level Image Tokenization
Authors: Delong Chen; Samuel Cahyawijaya; Jianfeng Liu; Baoyuan Wang; Pascale Fung
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Patch-based image tokenization ignores the morphology of the visual world, limiting effective and efficient learning of image understanding. Inspired by subword tokenization, we introduce subobject-level adaptive token segmentation and explore several approaches, including superpixel, SAM, and a proposed Efficient and PanOptiC (EPOC) image tokenizer. Our EPOC combines boundary detection--a simple task that can be handled well by a compact model--with watershed segmentation, which inherently guarantees no pixels are left unsegmented. Intrinsic evaluations across 5 datasets demonstrate that EPOC's segmentation aligns well with human annotations of both object- and part-level visual morphology, producing more monosemantic tokens and offering substantial efficiency advantages. For extrinsic evaluation, we designed a token embedding that handles arbitrary-shaped tokens, and trained VLMs with different tokenizers on 4 datasets of object recognition and detailed captioning. The results reveal that subobject tokenization enables faster convergence and better generalization while using fewer visual tokens.
Active Fine-Tuning of Multi-Task Policies
Authors: Marco Bagatella; Jonas Hübotter; Georg Martius; Andreas Krause
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Pre-trained generalist policies are rapidly gaining relevance in robot learning due to their promise of fast adaptation to novel, in-domain tasks.
This adaptation often relies on collecting new demonstrations for a specific task of interest and applying imitation learning algorithms, such as behavioral cloning.
However, as soon as several tasks need to be learned, we must decide *which tasks should be demonstrated and how often?*
We study this multi-task problem and explore an interactive framework in which the agent *adaptively* selects the tasks to be demonstrated.
We propose AMF (Active Multi-task Fine-tuning), an algorithm to maximize multi-task policy performance under a limited demonstration budget by collecting demonstrations yielding the largest information gain on the expert policy.
We derive performance guarantees for AMF under regularity assumptions and demonstrate its empirical effectiveness to efficiently fine-tune neural policies in complex and high-dimensional environments.
A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO
Authors: Xingyu Zhou; Yulian Wu; Francesco Orabona
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: In this paper, we theoretically investigate the effects of noisy labels in offline alignment, with a focus on the interplay between privacy and robustness against adversarial corruption. Specifically, under linear modeling assumptions, we present a unified analysis covering both reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) under different privacy-corruption scenarios, such as Local differential privacy-then-Corruption (LTC), where human preference labels are privatized before being corrupted by an adversary, and Corruption-then-Local differential privacy (CTL), where labels are corrupted before privacy protection. Our analysis leverages a reduction framework that reduces the offline alignment problem under linear modeling assumptions to parameter estimation in logistic regression. This framework allows us to establish an interesting separation result between LTC and CTL, demonstrating that LTC presents a greater challenge than CTL in offline alignment, even under linear models. As important by-products, our findings also advance the state-of-the-art theoretical results in offline alignment under privacy-only or corruption-only scenarios.
Riemannian Diffusion Adaptation for Distributed Optimization on Manifolds
Authors: Xiuheng Wang; Ricardo Augusto Borsoi; Cédric Richard; Ali H. Sayed
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Online distributed optimization is particularly useful for solving optimization problems with streaming data collected by multiple agents over a network. When the solutions lie on a Riemannian manifold, such problems become challenging to solve, particularly when efficiency and continuous adaptation are required. This work tackles these challenges and devises a diffusion adaptation strategy for decentralized optimization over general manifolds. A theoretical analysis shows that the proposed algorithm is able to approach network agreement after sufficient iterations, which allows a non-asymptotic convergence result to be derived. We apply the algorithm to the online decentralized principal component analysis problem and Gaussian mixture model inference. Experimental results with both synthetic and real data illustrate its performance.
FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates
Authors: Xinyi Lu; Hao Zhang; Chenglin Li; Weijia Lu; ZHIFEI YANG; Wenrui Dai; xiaodong Zhang; Xiaofeng Ma; Can Zhang; Junni Zou; Hongkai Xiong
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The significant communication overhead and client data heterogeneity have posed an important challenge to current federated learning (FL) paradigm. Existing compression-based and optimization-based FL algorithms typically focus on addressing either the model compression challenge or the data heterogeneity issue individually, rather than tackling both of them. In this paper, we observe that by symbolizing the client model updates to be uploaded (i.e., normalizing the magnitude for each model parameter at local clients), the model heterogeneity, essentially stemmed from data heterogeneity, can be mitigated, and thereby helping improve the overall generalization performance of the globally aggregated model at the server. Inspired with this observation, and further motivated by the success of Lion optimizer in achieving the optimal performance on most tasks in the centralized learning, we propose a new FL algorithm, called FedSMU, which simultaneously reduces the communication overhead and alleviates the data heterogeneity issue. Specifically, FedSMU splits the standard Lion optimizer into the local updates and global execution, where only the symbol of client model updates commutes between the client and server. We theoretically prove the convergence of FedSMU for the general non-convex settings. Through extensive experimental evaluations on several benchmark datasets, we demonstrate that our FedSMU algorithm not only reduces the communication overhead, but also achieves a better generalization performance than the other compression-based and optimization-based baselines.
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Authors: Guiomar Pescador-Barrios; Sarah Lucie Filippi; Mark van der Wilk
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Many machine learning models require setting a parameter that controls their size before training, e.g. number of neurons in DNNs, or inducing points in GPs. Increasing capacity typically improves performance until all the information from the dataset is captured. After this point, computational cost keeps increasing, without improved performance. This leads to the question "How big is big enough?" 
We investigate this problem for Gaussian processes (single-layer neural networks) in continual learning. Here, data becomes available incrementally, and the final dataset size will therefore not be known before training, preventing the use of heuristics for setting a fixed model size. We develop a method to automatically adjust model size while maintaining near-optimal performance. 
Our experimental procedure follows the constraint that any hyperparameters must be set without seeing dataset properties, and we show that our method performs well across diverse datasets without the need to adjust its hyperparameter, showing it requires less tuning than others.
Ranked Entropy Minimization for Continual Test-Time Adaptation
Authors: Jisu Han; Jaemin Na; Wonjun Hwang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Test-time adaptation aims to adapt to realistic environments in an online manner by learning during test time.  Entropy minimization has emerged as a principal strategy for test-time adaptation due to its efficiency and adaptability. Nevertheless, it remains underexplored in continual test-time adaptation, where stability is more important. We observe that the entropy minimization method often suffers from model collapse, where the model converges to predicting a single class for all images due to a trivial solution. We propose ranked entropy minimization to mitigate the stability problem of the entropy minimization method and extend its applicability to continuous scenarios. Our approach explicitly structures the prediction difficulty through a progressive masking strategy. Specifically, it gradually aligns the model's probability distributions across different levels of prediction difficulty while preserving the rank order of entropy. The proposed method is extensively evaluated across various benchmarks, demonstrating its effectiveness through empirical results.
Optimizing Language Models for Inference Time Objectives using Reinforcement Learning
Authors: Yunhao Tang; Kunhao Zheng; Gabriel Synnaeve; Remi Munos
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In this work, we investigate the merits of explicitly optimizing for inference time algorithmic performance during model training. We show how optimizing for inference time performance can improve overall model efficacy. We consider generic inference time objectives with $k$ samples, with focus on pass@$k$ and majority voting as two main applications. With language model training on reasoning datasets, we showcase the performance trade-off enabled by training with such objectives. When training on code generation tasks, we show that the approach significantly improves pass@$k$ objectives compared to the baseline method.
CoMemo: LVLMs Need Image Context with Image Memory
Authors: Shi Liu; Weijie Su; Xizhou Zhu; Wenhai Wang; Jifeng Dai
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent advancements in Large Vision-Language Models built upon Large Language Models have established aligning visual features with LLM representations as the dominant paradigm. 
However, inherited LLM architectural designs introduce suboptimal characteristics for multimodal processing. 
First, LVLMs exhibit a bimodal distribution in attention allocation, leading to the progressive neglect of middle visual content as context expands. 
Second, conventional positional encoding schemes fail to preserve vital 2D structural relationships when processing dynamic high-resolution images. 
To address these limitations, we propose **CoMemo** - a dual-path architecture that combines a **Co**ntext image path with an image **Memo**ry path for visual processing, effectively alleviating visual information neglect. 
Additionally, we introduce RoPE-DHR, a novel positional encoding mechanism that employs thumbnail-based positional aggregation to maintain 2D spatial awareness while mitigating remote decay in extended sequences. 
Evaluations across seven benchmarks,including long-context comprehension, multi-image reasoning, and visual question answering, demonstrate CoMemo's superior performance compared to conventional LVLM architectures.
Project page is available at [https://lalbj.github.io/projects/CoMemo/](https://lalbj.github.io/projects/CoMemo/).
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization
Authors: Ziyu Gong; Jim Lim; David I. Inouye
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Distribution matching (DM) is a versatile domain-invariant representation learning technique that has been applied to tasks such as fair classification, domain adaptation, and domain translation. 
Non-parametric DM methods struggle with scalability and adversarial DM approaches suffer from instability and mode collapse.
While likelihood-based methods are a promising alternative, they often impose unnecessary biases through fixed priors or require explicit density models (e.g., flows) that can be challenging to train.
We address this limitation by introducing a novel approach to training likelihood-based DM using expressive score-based prior distributions.
Our key insight is that gradient-based DM training only requires the prior's score function---not its density---allowing us to train the prior via denoising score matching. 
This approach eliminates biases from fixed priors (e.g., in VAEs), enabling more effective use of geometry-preserving regularization, while avoiding the challenge of learning an explicit prior density model (e.g., a flow-based prior). Our method also demonstrates better stability and computational efficiency compared to other diffusion-based priors (e.g., LSGM). 
Furthermore, experiments demonstrate superior performance across multiple tasks, establishing our score-based method as a stable and effective approach to distribution matching. Source code available at https://github.com/inouye-lab/SAUB.
Feature Importance Metrics in the Presence of Missing Data
Authors: Henrik von Kleist; Joshua Wendland; Ilya Shpitser; Carsten Marr
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Feature importance metrics are critical for interpreting machine learning models and understanding the relevance of individual features. However, real-world data often exhibit missingness, thereby complicating how feature importance should be evaluated.  We introduce the distinction between two evaluation frameworks under missing data: (1) feature importance under the full data, as if every feature had been fully measured, and (2) feature importance under the observed data, where missingness is governed by the current measurement policy. While the full data perspective offers insights into the data generating process, it often relies on unrealistic assumptions and cannot guide decisions when missingness persists at model deployment. Since neither framework directly informs improvements in data collection, we additionally introduce the feature measurement importance gradient (FMIG), a novel, model-agnostic metric that identifies features that should be measured more frequently to enhance predictive performance. Using synthetic data, we illustrate key differences between these metrics and the risks of conflating them.
MODA: MOdular Duplex Attention for Multimodal Perception, Cognition, and Emotion Understanding
Authors: Zhicheng Zhang; Wuyou Xia; Chenxi Zhao; Zhou Yan; Xiaoqiang Liu; Yongjie Zhu; Wenyu Qin; Pengfei Wan; Di ZHANG; Jufeng Yang
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Multimodal large language models (MLLMs) recently showed strong capacity in integrating data among multiple modalities, empowered by generalizable attention architecture. Advanced methods predominantly focus on language-centric tuning while less exploring multimodal tokens mixed through attention, posing challenges in high-level tasks that require fine-grained cognition and emotion understanding. In this work, we identify the attention deficit disorder problem in multimodal learning, caused by inconsistent cross-modal attention and layer-by-layer decayed attention activation. To address this, we propose a novel attention mechanism, termed MOdular Duplex Attention (MODA), simultaneously conducting the inner-modal refinement and inter-modal interaction. MODA employs a correct-after-align strategy to effectively decouple modality alignment from cross-layer token mixing. In the alignment phase, tokens are mapped to duplex modality spaces based on the basis vectors, enabling the interaction between visual and language modality. Further, the correctness of attention scores is ensured through adaptive masked attention, which enhances the model's flexibility by allowing customizable masking patterns for different modalities. Extensive experiments on 21 benchmark datasets verify the effectiveness of MODA in perception, cognition, and emotion tasks.
Revisiting Neural Networks for Few-Shot Learning: A Zero-Cost NAS Perspective
Authors: Haidong Kang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Neural Architecture Search (NAS) has recently outperformed hand-designed networks in various artificial intelligence areas. However, previous works only target a pre-defined task. For a new task in few-shot learning (FSL) scenarios, the architecture is either searched from scratch, which is neither efficient nor flexible, or borrowed architecture from the ones obtained on other tasks, which may lead to sub-optimal. Can we select the best neural architectures without involving any training and eliminate a significant portion of the search cost for new tasks in FSL? In this work, we provide an affirmative answer by proposing a novel information bottleneck (IB) theory driven \textit{Few-shot Neural Architecture Search} (dubbed, IBFS) framework to address this issue. We first derive that the global convergence of Model-agnostic meta-learning (MAML) can be guaranteed by only considering the first-order loss landscape. Moreover, motivated by the observation that IB provides a unified view toward understanding machine learning models, we propose a novel Zero-Cost method tailored for FSL to rank and select architectures based on their \textit{expressivity} obtained by IB mechanisms. Extensive experiments show that IBFS achieves state-of-the-art performance in FSL without training, which demonstrates the effectiveness of our IBFS.
TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems
Authors: Siyang Liu; Han-Jia Ye
Venue: ICML 2025 (ICML 2025 poster)

Abstract: TabPFN has emerged as a promising in-context learning model for tabular data, capable of directly predicting the labels of test samples given labeled training examples. It has demonstrated competitive performance, particularly on small-scale classification tasks. However, despite its effectiveness, TabPFN still requires further refinement in several areas, including handling high-dimensional features, aligning with downstream datasets, and scaling to larger datasets.
In this paper, we revisit existing variants of TabPFN and observe that most approaches focus either on reducing bias or variance, often neglecting the need to address the other side, while also increasing inference overhead. To fill this gap, we propose  Beta (**B**agging and **E**ncoder-based Fine-tuning for **T**abPFN **A**daptation), a novel and effective method designed to *minimize both bias and variance*. To reduce bias, we introduce a lightweight encoder to better align downstream tasks with the pre-trained TabPFN. By increasing the number of encoders in a lightweight manner, Beta mitigates variance, thereby further improving the model’s performance. Additionally, bootstrapped sampling is employed to further  reduce the impact of data perturbations on the model, all while maintaining computational efficiency during inference. Our approach enhances TabPFN’s ability to handle high-dimensional data and scale to larger datasets. Experimental results on over 200 benchmark classification datasets demonstrate that Beta either outperforms or matches state-of-the-art methods.
REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective
Authors: Simon Geisler; Tom Wollschläger; M. H. I. Abdalla; Vincent Cohen-Addad; Johannes Gasteiger; Stephan Günnemann
Venue: ICML 2025 (ICML 2025 poster)

Abstract: To circumvent the alignment of large language models (LLMs), current optimization-based adversarial attacks usually craft adversarial prompts by maximizing the likelihood of a so-called affirmative response. An affirmative response is a manually designed start of a harmful answer to an inappropriate request. While it is often easy to craft prompts that yield a substantial likelihood for the affirmative response, the attacked model frequently does not complete the response in a harmful manner. Moreover, the affirmative objective is usually not adapted to model-specific preferences and essentially ignores the fact that LLMs output a distribution over responses. If low attack success under such an objective is taken as a measure of robustness, the true robustness might be grossly overestimated. To alleviate these flaws, we propose an adaptive and semantic optimization problem over the population of responses. We derive a generally applicable objective via the REINFORCE policy-gradient formalism and demonstrate its efficacy with the state-of-the-art jailbreak algorithms Greedy Coordinate Gradient (GCG) and Projected Gradient Descent (PGD). For example, our objective doubles the attack success rate (ASR) on Llama3 and increases the ASR from 2\% to 50\% with circuit breaker defense.
Online Pre-Training for Offline-to-Online Reinforcement Learning
Authors: Yongjae Shin; Jeonghye Kim; Whiyoung Jung; Sunghoon Hong; Deunsol Yoon; Youngsoo Jang; Geon-Hyeong Kim; Jongseong Chae; Youngchul Sung; Kanghoon Lee; Woohyung Lim
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Offline-to-online reinforcement learning (RL) aims to integrate the complementary strengths of offline and online RL by pre-training an agent offline and subsequently fine-tuning it through online interactions. However, recent studies reveal that offline pre-trained agents often underperform during online fine-tuning due to inaccurate value estimation caused by distribution shift, with random initialization proving more effective in certain cases. In this work, we propose a novel method, Online Pre-Training for Offline-to-Online RL (OPT), explicitly designed to address the issue of inaccurate value estimation in offline pre-trained agents. OPT introduces a new learning phase, Online Pre-Training, which allows the training of a new value function tailored specifically for effective online fine-tuning. Implementation of OPT on TD3 and SPOT demonstrates an average 30\% improvement in performance across a wide range of D4RL environments, including MuJoCo, Antmaze, and Adroit.
Understanding the Limits of Deep Tabular Methods with Temporal Shift
Authors: Haorun Cai; Han-Jia Ye
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Deep tabular models have demonstrated remarkable success on i.i.d. data, excelling in a variety of structured data tasks. 
However, their performance often deteriorates under temporal distribution shifts, where trends and periodic patterns are present in the evolving data distribution over time.
In this paper, we explore the underlying reasons for this failure in capturing temporal dependencies. 
We begin by investigating the training protocol, revealing a key issue in how the data is split for model training and validation.
While existing approaches typically use temporal ordering for splitting, we show that even a random split significantly improves model performance. 
By accounting for reducing training lag and validation bias to achieve better generalization ability, our proposed splitting protocol offers substantial improvements across a variety of methods.
Furthermore, we analyses how temporal data affects deep tabular representations, uncovering that these models often fail to capture crucial periodic and trend information. 
To address this gap, we introduce a plug-and-play temporal embedding based on Fourier series expansion to learn and incorporate temporal patterns, offering an adaptive approach to handle temporal shifts.
Our experiments demonstrate that this temporal embedding, combined with the improved splitting strategy, provides a more effective and robust framework for learning from temporal tabular data.