GenAI4ELab/papercli-papers huggingface dataset:
Feature out! Let Raw Image as Your Condition for Blind Face Restoration
Authors: Xinmin Qiu; Chen Gege; Bonan Li; Congying Han; Tiande Guo; Zicheng Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Blind face restoration (BFR), which involves converting low-quality (LQ) images into high-quality (HQ) images, remains challenging due to complex and unknown degradations. 
    While previous diffusion-based methods utilize feature extractors from LQ images as guidance, using raw LQ images directly as the starting point for the reverse diffusion process offers a theoretically optimal solution. 
    In this work, we propose Pseudo-Hashing Image-to-image Schrödinger Bridge (P-I2SB), a novel framework inspired by optimal mass transport problems, which enhances the restoration potential of Schrödinger Bridge (SB) by correcting data distributions and effectively learning the optimal transport path between any two data distributions.
    Notably, we theoretically explore and identify that existing methods are limited by the optimality and reversibility of solutions in SB, leading to suboptimal performance. 
    Our approach involves preprocessing HQ images during training by hashing them into pseudo-samples according to a rule related to LQ images, ensuring structural similarity in distribution.
    This guarantees optimal and reversible solutions in SB, enabling the inference process to learn effectively and allowing P-I2SB to achieve state-of-the-art results in BFR, with more natural textures and retained inference speed compared to previous methods.
Learning Imperfect Information Extensive-form Games with Last-iterate Convergence under Bandit Feedback
Authors: Canzhe Zhao; Yutian Cheng; Jing Dong; Baoxiang Wang; Shuai Li
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We investigate learning approximate Nash equilibrium (NE) policy profiles in two-player zero-sum imperfect information extensive-form games (IIEFGs) with last-iterate convergence guarantees. Existing algorithms either rely on full-information feedback or provide only asymptotic convergence rates. In contrast, we focus on the bandit feedback setting, where players receive feedback solely from the rewards associated with the experienced information set and action pairs in each episode. Our proposed algorithm employs a negentropy regularizer weighted by a "virtual transition" over the information set-action space to facilitate an efficient approximate policy update. Through a carefully designed virtual transition and leveraging the entropy regularization technique, we demonstrate finite-time last-iterate convergence to the NE with a rate of $\widetilde{\mathcal{O}}(k^{-\frac{1}{8}})$ under bandit feedback in each episode $k$. Empirical evaluations across various IIEFG instances show its competitive performance compared to baseline methods.
C2IQL: Constraint-Conditioned Implicit Q-learning for Safe Offline Reinforcement Learning
Authors: Zifan LIU; Xinran Li; Jun Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Safe offline reinforcement learning aims to develop policies that maximize cumulative rewards while satisfying safety constraints without the need for risky online interaction. However, existing methods often struggle with the out-of-distribution (OOD) problem, leading to potentially unsafe and suboptimal policies. To address this issue, we first propose Constrained Implicit Q-learning (CIQL), a novel algorithm designed to avoid the OOD problem. In particular, CIQL expands the implicit update of 
reward value functions to constrained settings and then estimates cost value functions under the same implicit policy. Despite its advantages, the further performance improvement of CIQL is still hindered by the inaccurate discounted approximations of constraints. Thus, we further propose Constraint-Conditioned Implicit Q-learning (C2IQL). Building upon CIQL, C2IQL employs a cost reconstruction model to derive non-discounted cumulative costs from discounted values and incorporates a flexible, constraint-conditioned mechanism to accommodate dynamic safety constraints. Experiment results on DSRL benchmarks demonstrate the superiority of C2IQL compared to baseline methods in achieving higher rewards while guaranteeing safety constraints under different threshold conditions.
Learning to Generate Projections for Reducing Dimensionality of Heterogeneous Linear Programming Problems
Authors: Tomoharu Iwata; Shinsaku Sakaue
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We propose a data-driven method for reducing the dimensionality of linear programming problems (LPs) by generating instance-specific projection matrices using a neural network-based model. Once the model is trained using multiple LPs by maximizing the expected objective value, we can efficiently find high-quality feasible solutions of newly given LPs. Our method can shorten the computational time of any LP solvers due to its solver-agnostic nature, it can provide feasible solutions by relying on projection that reduces the number of variables, and it can handle LPs of different sizes using neural networks with permutation equivariance and invariance. We also provide a theoretical analysis of the generalization bound for learning a neural network to generate projection matrices that reduce the size of LPs. Our experimental results demonstrate that our method can obtain solutions with higher quality than the existing methods, while its computational time is significantly shorter than solving the original LPs.
Better to Teach than to Give: Domain Generalized Semantic Segmentation via Agent Queries with Diffusion Model Guidance
Authors: Fan Li; Xuan Wang; Min Qi; Zhaoxiang Zhang; Yuelei Xu
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Domain Generalized Semantic Segmentation (DGSS) trains a model on a labeled source domain to generalize to unseen target domains with consistent contextual distribution and varying visual appearance.
Most existing methods rely on domain randomization or data generation but struggle to capture the underlying scene distribution, resulting in the loss of useful semantic information. 
Inspired by the diffusion model's capability to generate diverse variations within a given scene context, we consider harnessing its rich prior knowledge of scene distribution to tackle the challenging DGSS task.
In this paper, we propose a novel agent \textbf{Query}-driven learning framework based on \textbf{Diff}usion model guidance for DGSS, named QueryDiff. 
Our recipe comprises three key ingredients: (1) generating agent queries from segmentation features to aggregate semantic information about instances within the scene; 
(2) learning the inherent semantic distribution of the scene through agent queries guided by diffusion features; 
(3) refining segmentation features using optimized agent queries for robust mask predictions.
Extensive experiments across various settings demonstrate that our method significantly outperforms previous state-of-the-art methods. 
Notably, it enhances the model's ability to generalize effectively to extreme domains, such as cubist art styles. Code is available at https://github.com/FanLiHub/QueryDiff.
Understanding Model Ensemble in Transferable Adversarial Attack
Authors: Wei Yao; Zeliang Zhang; Huayi Tang; Yong Liu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Model ensemble adversarial attack has become a powerful method for generating transferable adversarial examples that can target even unknown models, but its theoretical foundation remains underexplored. To address this gap, we provide early theoretical insights that serve as a roadmap for advancing model ensemble adversarial attack. We first define transferability error to measure the error in adversarial transferability, alongside concepts of diversity and empirical model ensemble Rademacher complexity. We then decompose the transferability error into vulnerability, diversity, and a constant, which rigidly explains the origin of transferability error in model ensemble attack: the vulnerability of an adversarial example to ensemble components, and the diversity of ensemble components. Furthermore, we apply the latest mathematical tools in information theory to bound the transferability error using complexity and generalization terms, validating three practical guidelines for reducing transferability error: (1) incorporating more surrogate models, (2) increasing their diversity, and (3) reducing their complexity in cases of overfitting. Finally, extensive experiments with 54 models validate our theoretical framework, representing a significant step forward in understanding transferable model ensemble adversarial attacks.
Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion
Authors: Anle Ke; Xu Zhang; Tong Chen; Ming Lu; Chao Zhou; Jiawen Gu; Zhan Ma
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Existing multimodal large model-based image compression frameworks often rely on a fragmented integration of semantic retrieval, latent compression, and generative models, resulting in suboptimal performance in both reconstruction fidelity and coding efficiency. To address these challenges, we propose a residual-guided ultra lowrate image compression named ResULIC, which incorporates residual signals into both semantic retrieval and the diffusion-based generation process. Specifically, we introduce Semantic Residual Coding (SRC) to capture the semantic disparity between the original image and its compressed latent representation. A perceptual fidelity optimizer is further applied for superior reconstruction quality. Additionally, we present the Compression-aware Diffusion Model (CDM), which establishes an optimal alignment between bitrates and diffusion time steps, improving compression-reconstruction synergy. Extensive experiments demonstrate the effectiveness of ResULIC, achieving superior objective and subjective performance compared to state-of-the-art diffusion-based methods with -80.7\%, -66.3\% BD-rate saving in terms of LPIPS and FID.
Efficient Skill Discovery via Regret-Aware Optimization
Authors: He Zhang; Ming Zhou; Shaopeng Zhai; Ying Sun; Hui Xiong
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Unsupervised skill discovery aims to learn diverse and distinguishable behaviors in open-ended reinforcement learning.
For the existing methods, they focus on improving the diversity via pure exploration, mutual information optimization and learning temporal representation. 
Despite they perform well on exploration, they remain limited in terms of efficiency, especially for the high-dimensional situations.
In this work, we frame the skill discovery as a min-max game of skill generation and policy learning, proposing a regret-aware method on top of temporal representation learning that expands the discovered skill space along the direction of upgradable policy strength.
The key insight behind the proposed method is that the skill discovery is adversarial to the policy learning, i.e., skills with weak strength should be further explored while less exploration for the skills with converged strength.
As an implementation, we score the degree of strength convergence with regret, and guide the skill discovery with a learnable skill generator. To avoid degeneration, the skill generation comes from an upgradable population of skill generators.
We conduct experiments on environments with varying complexities and dimension sizes.
Empirical results show that our method outperforms baselines on both efficiency and diversity.
Moreover, our method achieves 15\% zero-shot improvement on high-dimensional environments, compared to existing methods.
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
Authors: Gregoire Fournier; Sourav Medya
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graph neural networks (GNNs) have been widely used in various domains such as social networks, molecular biology, or recommendation systems. Concurrently, different explanations methods of GNNs have arisen to complement its blackbox nature. Explanations of the GNNs’ predictions can be categorized into two types—factual and counterfactual. Given a GNN trained on binary classification into “accept” and “reject” classes, a global counterfactual explanation consists in generating a small set of “accept” graphs relevant to all of the input “reject” graphs. The transformation of a “reject” graph into an “accept” graph is called a recourse. A common recourse explanation is a small set of recourse, from which every “reject” graph can be turned into an “accept” graph. Although local counterfactual explanations have been studied extensively, the problem of finding common recourse for global counterfactual explanation remains unexplored, particularly for GNNs. In this paper, we formalize the common recourse explanation problem, and design an effective algorithm, COMRECGC, to solve it. We benchmark our algorithm against strong baselines on four different real-world graphs datasets and demonstrate the superior performance of COMRECGC against the competitors. We also compare the common recourse explanations to the graph counterfactual explanation, showing that common recourse explanations are either comparable or superior, making them worth considering for applications such as drug discovery or computational biology.
TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
Authors: Peiyuan Liu; Beiliang Wu; Yifan Hu; Naiqi Li; Tao Dai; Jigang Bao; Shu-Tao Xia
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Non-stationarity poses significant challenges for multivariate time series forecasting due to the inherent short-term fluctuations and long-term trends that can lead to spurious regressions or obscure essential long-term relationships. Most existing methods either eliminate or retain non-stationarity without adequately addressing its distinct impacts on short-term and long-term modeling. Eliminating non-stationarity is essential for avoiding spurious regressions and capturing local dependencies in short-term modeling, while preserving it is crucial for revealing long-term cointegration across variates. In this paper, we propose TimeBridge, a novel framework designed to bridge the gap between non-stationarity and dependency modeling in long-term time series forecasting. By segmenting input series into smaller patches, TimeBridge applies Integrated Attention to mitigate short-term non-stationarity and capture stable dependencies within each variate, while Cointegrated Attention preserves non-stationarity to model long-term cointegration across variates. Extensive experiments show that TimeBridge consistently achieves state-of-the-art performance in both short-term and long-term forecasting. Additionally, TimeBridge demonstrates exceptional performance in financial forecasting on the CSI 500 and S&P 500 indices, further validating its robustness and effectiveness. Code is available at https://github.com/Hank0626/TimeBridge.
Olica: Efficient Structured Pruning of Large Language Models without Retraining
Authors: Jiujun He; Huazhen Lin
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Most existing structured pruning methods for  Large Language Models (LLMs) require substantial computational  and data resources for retraining to reestablish the corrupted correlations, making them prohibitively expensive. To address this, we propose an efficient pruning framework for LLMs called Orthogonal Neuron Decomposition and Linear Calibration  (Olica),  which eliminates the need for retraining.  A key observation is that the multi-head attention (MHA) layer depends on two types of matrix products (i.e., ${\rm W}_q{\rm W}^{\top}_k$ and ${\rm W}_v{\rm W}^{\top}_o$). By treating these matrix products as unified entities and applying principal component analysis (PCA), we extract the most important information to compress LLMs without sacrificing accuracy or disrupting their original structure. Consequently, retraining becomes unnecessary. Moreover, a fast decomposition method is  devised, reducing the  complexity of PCA by a factor of the square of the number of attention heads. Additionally,  to mitigate error accumulation problem caused by pruning the feed-forward network (FFN) layer, we introduce a linear calibration  method to reconstruct the residual errors of a pruned layer using two low-rank matrices. By leveraging singular value decomposition (SVD) on the solution of the least-squares problem, these matrices are obtained without requiring  retraining.  Extensive experiments show that the proposed Olica is efficient in terms of data usage, GPU memory, and running time, while delivering superior performance across multiple benchmarks.
Knowledge Swapping via Learning and Unlearning
Authors: Mingyu Xing; Lechao Cheng; Shengeng Tang; Yaxiong Wang; Zhun Zhong; Meng Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce Knowledge Swapping, a novel task designed to selectively regulate knowledge of a pretrained model by enabling the forgetting of user-specified information, retaining essential knowledge, and acquiring new knowledge simultaneously.  By delving into the analysis of knock-on feature hierarchy, we find that incremental learning typically progresses from low-level representations to higher-level semantics, whereas forgetting tends to occur in the opposite direction—starting from high-level semantics and moving down to low-level features. Building upon this, we propose to benchmark the knowledge swapping task with the strategy of Learning Before Forgetting. Comprehensive experiments on various tasks like image classification, object detection, and semantic segmentation validate the effectiveness of the proposed strategy. The source code is available at https://github.com/xingmingyu123456/KnowledgeSwapping.
Learn Beneficial Noise as Graph Augmentation
Authors: Siqi Huang; Yanchen Xu; Hongyuan Zhang; Xuelong Li
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Although graph contrastive learning (GCL) has been widely investigated,  it is still a challenge to generate effective and stable graph augmentations. Existing methods often apply heuristic augmentation like random edge dropping, which may disrupt important graph structures and result in unstable GCL performance. In this paper, we propose **P**ositive-**i**ncentive **N**oise driven **G**raph **D**ata **A**ugmentation (PiNGDA), where positive-incentive noise (pi-noise) scientifically analyzes the beneficial effect of noise under the information theory. To bridge the standard GCL and pi-noise framework, we design a Gaussian auxiliary variable to convert the loss function to information entropy. We prove that the standard GCL with pre-defined augmentations is equivalent to estimate the beneficial noise via the point estimation.  Following our analysis, PiNGDA is derived from learning the beneficial noise on both topology and attributes through a trainable noise generator for graph augmentations, instead of the simple estimation. Since the generator learns how to produce beneficial perturbations on graph topology and node attributes, PiNGDA is more reliable compared with the existing methods. Extensive experimental results validate the effectiveness and stability of PiNGDA.
Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension
Authors: Yijun Dong; Yicheng Li; Yunai Li; Jason D. Lee; Qi Lei
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Weak-to-strong (W2S) generalization is a type of finetuning (FT) where a strong (large) student model is trained on pseudo-labels generated by a weak teacher. Surprisingly, W2S FT often outperforms the weak teacher. We seek to understand this phenomenon through the observation that FT often occurs in intrinsically low-dimensional spaces. Leveraging the low intrinsic dimensionality of FT, we analyze W2S in the ridgeless regression setting from a variance reduction perspective. For a strong student-weak teacher pair with sufficiently expressive low-dimensional feature subspaces $\mathcal{V}_s, \mathcal{V}_w$, we provide an exact characterization of the variance that dominates the generalization error of W2S. This unveils a virtue of discrepancy between the strong and weak models in W2S: the variance of the weak teacher is inherited by the strong student in $\mathcal{V}_s \cap \mathcal{V}_w$, while reduced by a factor of $\mathrm{dim}(\mathcal{V}_s)/N$ in the subspace of discrepancy $\mathcal{V}_w \setminus \mathcal{V}_s$ with $N$ pseudo-labels for W2S. Our analysis further casts light on the sample complexities and the scaling of performance gap recovery in W2S. The analysis is supported by experiments on synthetic regression problems, as well as real vision and NLP tasks.
STD-FD: Spatio-Temporal Distribution Fitting Deviation for AIGC Forgery Identification
Authors: Hengrui Lou; Zunlei Feng; Jinsong Geng; Erteng Liu; Jie Lei; Lechao Cheng; Jie Song; Mingli Song; Yijun Bei
Venue: ICML 2025 (ICML 2025 poster)

Abstract: With the rise of AIGC technologies, particularly diffusion models, highly realistic fake images that can deceive human visual perception has become feasible. Consequently, various forgery detection methods have emerged. However, existing methods treat the generation process of fake images as either a black-box or an auxiliary tool, offering limited insights into its underlying mechanisms. In this paper, we propose Spatio-Temporal Distribution Fitting Deviation (STD-FD) for AIGC forgery detection, which explores the generative process in detail. By decomposing and reconstructing data within generative diffusion models, initial experiments reveal temporal distribution fitting deviations during the image reconstruction process. These deviations are captured through reconstruction noise maps for each spatial semantic unit, derived via a super-resolution algorithm. Critical discriminative patterns, termed DFactors, are identified through statistical modeling of these deviations. Extensive experiments show that STD-FD effectively captures distribution patterns in AIGC-generated data, demonstrating strong robustness and generalizability while outperforming state-of-the-art (SOTA) methods on major datasets. The source code is available at [this link](https://github.com/HengruiLou/STDFD).
Low-Rank Adapting Models for Sparse Autoencoders
Authors: Matthew Chen; Joshua Engels; Max Tegmark
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Sparse autoencoders (SAEs) aim to decompose language model representations into a sparse set of linear latent vectors. Recent works have improved SAEs using language model gradients, but these techniques require many expensive backward passes during training and still cause a significant increase in cross entropy loss when SAE reconstructions are inserted into the model. In this work, we improve on these limitations by taking a fundamentally different approach: we use low-rank adaptation (LoRA) to finetune the *language model itself* around a previously trained SAE. We analyze our method across SAE sparsity, SAE width, language model size, LoRA rank, and model layer on the Gemma Scope family of SAEs. In these settings, our method reduces the cross entropy loss gap by 30% - 55% when SAEs are inserted during the forward pass. We also find that compared to end-to-end (e2e) SAEs, our approach achieves the same downstream cross entropy loss 3$\times$ to 20$\times$ faster on Gemma-2-2B and 2$\times$ to 10$\times$ faster on Llama-3.2-1B. We further show that our technique improves downstream metrics and can adapt multiple SAEs at once. Our results demonstrate that improving model interpretability is not limited to post-hoc SAE training; Pareto improvements can also be achieved by directly optimizing the model itself.