GenAI4ELab/papercli-papers huggingface dataset:
Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination
Authors: Zhihan Zhu; Yanhao Zhang; Yong Xia
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper introduces two novel criteria: one for feature selection and another for feature elimination in the context of best subset selection, which is a benchmark problem in statistics and machine learning. From the perspective of optimization, we revisit the classical selection and elimination criteria in traditional best subset selection algorithms, revealing that these classical criteria capture only partial variations of the objective function after the entry or exit of features. By formulating and solving optimization subproblems for feature entry and exit exactly, new selection and elimination criteria are proposed, proved as the optimal decisions for the current entry-and-exit process compared to classical criteria. Replacing the classical selection and elimination criteria with the proposed ones generates a series of enhanced best subset selection algorithms. These generated algorithms not only preserve the theoretical properties of the original algorithms but also achieve significant meta-gains without increasing computational cost across various scenarios and evaluation metrics on multiple tasks such as compressed sensing and sparse regression.
Lean and Mean Adaptive Optimization via Subset-Norm and Subspace-Momentum with Convergence Guarantees
Authors: Thien Hang Nguyen; Huy Nguyen
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce two complementary techniques for efficient optimization that reduce memory requirements while accelerating training of
large-scale neural networks. The first technique, Subset-Norm step size, generalizes AdaGrad-Norm and AdaGrad(-Coordinate) through step-size sharing. Subset-Norm (SN) reduces AdaGrad's memory footprint from $O(d)$ to $O(\sqrt{d})$, where $d$ is the model size. For non-convex smooth objectives under coordinate-wise sub-gaussian noise, we show a noise-adapted high-probability convergence guarantee with improved dimensional dependence of SN over existing methods. Our second technique, Subspace-Momentum, reduces the momentum state's memory footprint by restricting momentum to a low-dimensional subspace while performing SGD in the orthogonal complement. We prove high-probability convergence rates for Subspace-Momentum under standard assumptions. Empirical evaluation on pre-training and fine-tuning LLMs  demonstrates the effectiveness of our methods. For instance, combining Subset-Norm with Subspace-Momentum achieves Adam's validation perplexity for LLaMA 1B in approximately half the training
tokens (6.8B vs 13.1B) while reducing Adam's optimizer-states memory footprint by more than 80\% with minimal additional hyperparameter tuning.
DANCE: Dual Unbiased Expansion with Group-acquired Alignment for Out-of-distribution Graph Fairness Learning
Authors: Yifan Wang; Hourun Li; Yue Ling; Zhiping Xiao; Jia Yang; Changling Zhou; Wei Ju; Ming Zhang; Xiao Luo
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graph neural networks (GNNs) have shown strong performance in graph fairness learning, which aims to ensure that predictions are unbiased with respect to sensitive attributes. However, existing approaches usually assume that training and test data share the same distribution, which rarely holds in the real world. To tackle this challenge, we propose a novel approach named Dual Unbiased Expansion with Group-acquired Alignment (DANCE) for graph fairness learning under distribution shifts. The core idea of our DANCE is to synthesize challenging yet unbiased virtual graph data in both graph and hidden spaces, simulating distribution shifts from a data-centric view. Specifically, we introduce the unbiased Mixup in the hidden space, prioritizing minor groups to address the potential imbalance of sensitive attributes. Simultaneously, we conduct fairness-aware adversarial learning in the graph space to focus on challenging samples and improve model robustness. To further bridge the domain gap, we propose a group-acquired alignment objective that prioritizes negative pair groups with identical sensitive labels. Additionally, a representation disentanglement objective is adopted to decorrelate sensitive attributes and target representations for enhanced fairness. Extensive experiments demonstrate the superior effectiveness of the proposed DANCE.
TRUST-VLM: Thorough Red-Teaming for Uncovering Safety Threats in Vision-Language Models
Authors: Kangjie Chen; Li Muyang; Guanlin Li; Shudong Zhang; Shangwei Guo; Tianwei Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Vision-Language Models (VLMs) have become a cornerstone in multi-modal artificial intelligence, enabling seamless integration of visual and textual information for tasks such as image captioning, visual question answering, and cross-modal retrieval. Despite their impressive capabilities, these models often exhibit inherent vulnerabilities that can lead to safety failures in critical applications. 
Red-teaming is an important approach to identify and test system's vulnerabilities, but how to conduct red-teaming for contemporary VLMs is an unexplored area. 
In this paper, we propose a novel multi-modal red-teaming approach, TRUST-VLM, to enhance both the attack success rate and the diversity of successful test cases for VLMs. 
Specifically, TRUST-VLM is built upon the in-context learning to adversarially test a VLM on both image and text inputs. Furthermore, we involve feedback from the target VLM to improve the efficiency of test case generation. Extensive experiments show that TRUST-VLM not only outperforms traditional red-teaming techniques in generating diverse and effective adversarial cases but also provides actionable insights for model improvement. 
These findings highlight the importance of advanced red-teaming strategies in ensuring the reliability of VLMs.
CateKV: On Sequential Consistency for Long-Context LLM Inference Acceleration
Authors: Haoyun Jiang; Haolin li; jianwei zhang; Fei Huang; Qiang Hu; Minmin Sun; Shuai Xiao; Yong Li; Junyang Lin; Jiangchao Yao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large language models (LLMs) have demonstrated strong capabilities in handling long-context tasks, but processing such long contexts remains challenging due to the substantial memory requirements and inference latency. In this work, we discover that certain attention heads exhibit sequential consistency in their attention patterns, which can be persistently identified using a coefficient-of-variation-based algorithm. Inspired by this observation, we propose CateKV, a hybrid KV cache method that retains only critical token information for consistent heads, thereby reducing KV cache size and computational overhead, while preserving the majority of KV pairs in adaptive heads to ensure high accuracy. We show the unique characteristics of our algorithm and its extension with existing acceleration methods. Comprehensive evaluations on long-context benchmarks show that, while maintaining accuracy comparable to full attention, CateKV reduces memory usage by up to $2.72\times$ and accelerates decoding by $2.18\times$ in single-sample inputs, and boosts throughput by $3.96\times$ in batch scenarios.
An Online Statistical Framework for Out-of-Distribution Detection
Authors: Xinsong Ma; Xin Zou; Weiwei Liu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Out-of-distribution (OOD) detection  task  is significant   in  reliable and safety-critical applications.  Existing approaches primarily focus on developing  the powerful score function, but overlook the design of decision-making rules based on these score function. In contrast to prior studies, we rethink the OOD detection task from an perspective of online multiple hypothesis testing. We then propose a novel generalized  LOND (g-LOND) algorithm to solve the above problem. Theoretically, the g-LOND algorithm  controls false discovery rate  (FDR) at pre-specified level without the consideration for the dependence between the p-values. Furthermore, we prove that the false positive rate (FPR) of the g-LOND algorithm converges to zero in probability based on the  generalized Gaussian-like distribution family. Finally, the extensive experimental results verify the effectiveness of g-LOND algorithm for OOD detection.
Divide and Conquer: Exploring Language-centric Tree Reasoning for Video Question-Answering
Authors: Zhaohe Liao; Jiangtong Li; Siyu Sun; Qingyang Liu; Fengshun Xiao; Tianjiao Li; Qiang Zhang; Guang Chen; Li Niu; Changjun Jiang; Liqing Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Video Question-Answering (VideoQA) remains challenging in achieving advanced cognitive reasoning due to the uncontrollable and opaque reasoning processes in existing Multimodal Large Language Models (MLLMs). To address this issue, we propose a novel Language-centric Tree Reasoning (LTR) framework that targets on enhancing the reasoning ability of models. In detail, it recursively divides the original question into logically manageable parts and conquers them piece by piece, enhancing the reasoning capabilities and interpretability of existing MLLMs. Specifically, in the first stage, the LTR focuses on language to recursively generate a language-centric logical tree, which gradually breaks down the complex cognitive question into simple perceptual ones and plans the reasoning path through a RAG-based few-shot approach. In the second stage, with the aid of video content, the LTR performs bottom-up logical reasoning within the tree to derive the final answer along with the traceable reasoning path. Experiments across 11 VideoQA benchmarks demonstrate that our LTR framework significantly improves both accuracy and interpretability compared to state-of-the-art MLLMs. To our knowledge, this is the first work to implement a language-centric logical tree to guide MLLM reasoning in VideoQA, paving the way for language-centric video understanding from perception to cognition.
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling
Authors: Hao Li; Yu-Hao Huang; Chang Xu; Viktor Schlegel; Renhe Jiang; Riza Batista-Navarro; Goran Nenadic; Jiang Bian
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Time-series Generation (TSG) is a prominent research area with broad applications in simulations, data augmentation, and counterfactual analysis. While existing methods have shown promise in unconditional single-domain TSG, real-world applications demand for cross-domain approaches capable of controlled generation tailored to domain-specific constraints and instance-level requirements. 
In this paper, we argue that text can provide semantic insights, domain information and instance-specific temporal patterns, to guide and improve TSG.  We introduce ``Text-Controlled TSG'', a task focused on generating realistic time series by incorporating textual descriptions.
To address data scarcity in this setting, we propose a novel LLM-based Multi-Agent framework that synthesizes diverse, realistic text-to-TS datasets. Furthermore, we introduce Bridge, a hybrid text-controlled TSG framework that integrates semantic prototypes with text description for supporting domain-level guidance. This approach achieves state-of-the-art generation fidelity on 11 of 12 datasets, and improves controllability by up to 12% on MSE and 6% MAE compared to no text input generation, highlighting its potential for generating tailored time-series data.
Not All Tokens Matter All The Time: Dynamic Token Aggregation Towards Efficient Detection Transformers
Authors: Jiacheng Cheng; Xiwen Yao; Xiang Yuan; Junwei Han
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The substantial computational demands of detection transformers (DETRs) hinder their deployment in resource-constrained scenarios, with the encoder consistently emerging as a critical bottleneck. A promising solution lies in reducing token redundancy within the encoder. However, existing methods perform static sparsification while ignoring the varying importance of tokens across different levels and encoder blocks for object detection, leading to suboptimal sparsification and performance degradation. In this paper, we propose **Dynamic DETR** (**Dynamic** token aggregation for **DE**tection **TR**ansformers), a novel strategy that leverages inherent importance distribution to control token density and performs multi-level token sparsification. Within each stage, we apply a proximal aggregation paradigm for low-level tokens to maintain spatial integrity, and a holistic strategy for high-level tokens to capture broader contextual information. Furthermore, we propose center-distance regularization to align the distribution of tokens throughout the sparsification process, thereby facilitating the representation consistency and effectively preserving critical object-specific patterns. Extensive experiments on canonical DETR models demonstrate that Dynamic DETR is broadly applicable across various models and consistently outperforms existing token sparsification methods.
SEMU: Singular Value Decomposition for Efficient Machine Unlearning
Authors: Marcin Sendera; Łukasz Struski; Kamil Książek; Kryspin Musiol; Jacek Tabor; Dawid Damian Rymarczyk
Venue: ICML 2025 (ICML 2025 poster)

Abstract: While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pressing challenges in AI safety, machine unlearning (MU) has become increasingly critical to meet upcoming safety regulations. Most existing MU approaches focus on altering the most significant parameters of the model. However, these methods often require fine-tuning substantial portions of the model, resulting in high computational costs and training instabilities, which are typically mitigated by access to the original training dataset.

In this work, we address these limitations by leveraging Singular Value Decomposition (SVD) to create a compact, low-dimensional projection that enables the selective forgetting of specific data points. We propose Singular Value Decomposition for Efficient Machine Unlearning (SEMU), a novel approach designed to optimize MU in two key aspects. First, SEMU minimizes the number of model parameters that need to be modified, effectively removing unwanted knowledge while making only minimal changes to the model's weights. Second, SEMU eliminates the dependency on the original training dataset, preserving the model's previously acquired knowledge without additional data requirements.

Extensive experiments demonstrate that SEMU achieves competitive performance while significantly improving efficiency in terms of both data usage and the number of modified parameters.
FlexControl: Computation-Aware Conditional Control with Differentiable Router for Text-to-Image Generation
Authors: Zheng Fang; Lichuan Xiang; Xu Cai; Kaicheng Zhou; Hongkai Wen
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Spatial conditioning control offers a powerful way to guide diffusion‐based generative models. Yet, most implementations (e.g., ControlNet) rely on ad-hoc heuristics to choose which network blocks to control — an approach that varies unpredictably with different tasks. To address this gap, we propose FlexControl, a novel framework that equips all diffusion blocks with control signals during training and employs a trainable gating mechanism to dynamically select which control signal to activate at each denoising step. By introducing a computation-aware loss, we can encourage the control signal to activate only when it benefits the generation quality. By eliminating manual control unit selection, FlexControl enhances adaptability across diverse tasks and streamlines the design pipeline with computation-aware training loss in an end-to-end training manner. Through comprehensive experiments on both UNet and DiT architectures on different control methods, we show that our method can upgrade existing controllable generative models in certain key aspects of interest. As evidenced by both quantitative and qualitative evaluations, FlexControl preserves or enhances image fidelity while also reducing computational overhead by selectively activating the most relevant blocks to control. These results underscore the potential of a flexible, data‐driven approach for controlled diffusion and open new avenues for efficient generative model design. The code will soon be available at https://github.com/Daryu-Fan/FlexControl.
Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning
Authors: Yang Li; Jiale Ma; Yebin Yang; Qitian Wu; Hongyuan Zha; Junchi Yan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Predicting labels directly from data has been the standard in label learning tasks, e.g., supervised learning, where models often prioritize feature compression and extraction from inputs under the assumption that label information is less complex. However, recent prediction tasks often face predicting complex labels, exacerbating the challenge of learning mappings from learned features to high-fidelity label representations. To this end, we draw inspiration from the consistency training concept in generative consistency models and propose predictive consistency learning (PCL), a novel learning paradigm that decomposes the full label information into a progressive learning procedure, mitigating the label capture challenge. Besides data inputs, PCL additionally receives input from noise-perturbed labels as an additional reference, pursuing predictive consistency across different noise levels. It simultaneously learns the relationship between latent features and a spectrum of label information, which enables progressive learning for complex predictions and allows multi-step inference analogous to gradual denoising, thereby enhancing the prediction quality. Experiments on vision, text, and graph tasks show the superiority of PCL over conventional supervised training in complex label prediction tasks.
Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector
Authors: Qirui Wu; Shizhou Zhang; De Cheng; Yinghui Xing; Di Xu; PENG WANG; Yanning Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Catastrophic forgetting is a critical chanllenge for incremental object detection (IOD). Most existing methods treat the detector monolithically, relying on instance replay or knowledge distillation without analyzing component-specific forgetting. Through dissection of Faster R-CNN, we reveal a key insight: Catastrophic forgetting is predominantly localized to the RoI Head classifier, while regressors retain robustness across incremental stages. This finding challenges conventional assumptions, motivating us to develop a framework termed NSGP-RePRE. Regional Prototype Replay (RePRE) mitigates classifier forgetting via replay of two types of prototypes: coarse prototypes represent class-wise semantic centers of RoI features, while fine-grained prototypes model intra-class variations. Null Space Gradient Projection (NSGP) is further introduced to eliminate prototype-feature misalignment by updating the feature extractor in directions orthogonal to subspace of old inputs via gradient projection, aligning RePRE with incremental learning dynamics. Our simple yet effective design allows NSGP-RePRE to achieve state-of-the-art performance on the Pascal VOC and MS COCO datasets under various settings. Our work not only advances IOD methodology but also provide pivotal insights for catastrophic forgetting mitigation in IOD. Code will be available soon.
Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Authors: Hongbi Zhou; Zhangkai Ni
Venue: ICML 2025 (ICML 2025 poster)

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis. However, existing methods struggle to adaptively optimize the distribution of Gaussian primitives based on scene characteristics, making it challenging to balance reconstruction quality and efficiency. Inspired by human perception, we propose scene-adaptive perceptual densification for Gaussian Splatting (Perceptual-GS), a novel framework that integrates perceptual sensitivity into the 3DGS training process to address this challenge. We first introduce a perception-aware representation that models human visual sensitivity while constraining the number of Gaussian primitives. Building on this foundation, we develop a perceptual sensitivity-adaptive distribution to allocate finer Gaussian granularity to visually critical regions, enhancing reconstruction quality and robustness. Extensive evaluations on multiple datasets, including BungeeNeRF for large-scale scenes, demonstrate that Perceptual-GS achieves state-of-the-art performance in reconstruction quality, efficiency, and robustness. The code is publicly available at: https://github.com/eezkni/Perceptual-GS
MP-Nav: Enhancing Data Poisoning Attacks against Multimodal Learning
Authors: Jingfeng Zhang; Prashanth Krishnamurthy; Naman Patel; Anthony Tzes; Farshad Khorrami
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Despite the success of current multimodal learning at scale, its susceptibility to data poisoning attacks poses security concerns in critical applications. Attacker can manipulate model behavior by injecting maliciously crafted yet minute instances into the training set, stealthily mismatching distinct concepts. Recent studies have manifested the vulnerability by poisoning multimodal tasks such as Text-Image Retrieval (TIR) and Visual Question Answering (VQA). However, the current attacking method only rely on random choice of concepts for misassociation and random instance selections for injecting the poisoning noise, which often achieves the suboptimal effect and even risks failure due to the dilution of poisons by the large number of benign instances. This study introduces MP-Nav (Multimodal Poison Navigator), a plug-and-play module designed to evaluate and even enhance data poisoning attacks against multimodal models. MP-Nav operates at both the concept and instance levels, identifying semantically similar concept pairs and selecting robust instances to maximize the attack efficacy. The experiments corroborate MP-Nav can significantly improve the efficacy of state-of-the-art data poisoning attacks such as AtoB and ShadowCast in multimodal tasks, and maintain model utility across diverse datasets. Notably, this study underscores the vulnerabilities of multimodal models and calls for the counterpart defenses.
Accurate and Efficient World Modeling with Masked Latent Transformers
Authors: Maxime Burchi; Radu Timofte
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The Dreamer algorithm has recently obtained remarkable performance across diverse environment domains by training powerful agents with simulated trajectories. However, the compressed nature of its world model's latent space can result in the loss of crucial information, negatively affecting the agent's performance. Recent approaches, such as $\Delta$-IRIS and DIAMOND, address this limitation by training more accurate world models. However, these methods require training agents directly from pixels, which reduces training efficiency and prevents the agent from benefiting from the inner representations learned by the world model. In this work, we propose an alternative approach to world modeling that is both accurate and efficient. We introduce EMERALD (Efficient MaskEd latent tRAnsformer worLD model), a world model using a spatial latent state with MaskGIT predictions to generate accurate trajectories in latent space and improve the agent performance. On the Crafter benchmark, EMERALD achieves new state-of-the-art performance, becoming the first method to surpass human experts performance within 10M environment steps. Our method also succeeds to unlock all 22 Crafter achievements at least once during evaluation.