GenAI4ELab/papercli-papers huggingface dataset:
SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
Authors: Shuo Yang; Bardh Prenkaj; Gjergji Kasneci
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Shortcut learning undermines model generalization to out-of-distribution data. While the literature attributes shortcuts to biases in superficial features, we show that imbalances in the semantic distribution of sample embeddings induce spurious semantic correlations, compromising model robustness. To address this issue, we propose SCISSOR (Semantic Cluster Intervention for Suppressing ShORtcut), a Siamese network-based debiasing approach that remaps the semantic space by discouraging latent clusters exploited as shortcuts. Unlike prior data-debiasing approaches, SCISSOR eliminates the need for data augmentation and rewriting. 
We evaluate SCISSOR on 6 models across 4 benchmarks: Chest-XRay and Not-MNIST in computer vision, and GYAFC and Yelp in NLP tasks. Compared to several baselines, SCISSOR reports +5.3 absolute points in F1 score on GYAFC, +7.3 on Yelp, +7.7 on Chest-XRay, and +1 on Not-MNIST. SCISSOR is also highly advantageous for lightweight models with ∼9.5% improvement on F1 for ViT on computer vision datasets and ∼11.9% for BERT on NLP. Our study redefines the landscape of model generalization by addressing overlooked semantic biases, establishing SCISSOR as a foundational framework for mitigating shortcut learning and fostering more robust, bias-resistant AI systems.
Preference Controllable Reinforcement Learning with Advanced Multi-Objective Optimization
Authors: Yucheng Yang; Tianyi Zhou; Mykola Pechenizkiy; Meng Fang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Practical reinforcement learning (RL) usually requires agents to be optimized for multiple potentially conflicting criteria, e.g. speed vs. safety. 
Although Multi-Objective RL (MORL) algorithms have been studied in previous works, their trained agents often cover limited Pareto optimal solutions and they lack precise controllability of the delicate trade-off among multiple objectives. Hence, the resulting agent is not versatile in aligning with customized requests from different users. 
To bridge the gap, we develop the ``Preference controllable (PC) RL'' framework, which trains a preference-conditioned meta-policy that takes user preference as input controlling the generated trajectories within the preference region on the Pareto frontier. The PCRL framework is compatible with advanced Multi-Objective Optimization~(MOO) algorithms that are rarely seen in previous MORL approaches. 

We also proposed a novel preference-regularized MOO algorithm specifically for PCRL. We provide a comprehensive theoretical analysis to justify its convergence and preference controllability.
We evaluate PCRL with different MOO algorithms against state-of-the-art MORL baselines in various challenging environments with up to six objectives. In these experiments, our proposed method exhibits significantly better controllability than existing approaches and can generate Pareto solutions with better diversity and utilities.
Zero-Shot Generalization of GNNs over Distinct Attribute Domains
Authors: Yangyi Shen; Jincheng Zhou; Beatrice Bevilacqua; Joshua Robinson; Charilaos Kanatsoulis; Jure Leskovec; Bruno Ribeiro
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Traditional Graph Neural Networks (GNNs) cannot generalize to new graphs with node attributes different from the training ones, making zero-shot generalization across different node attribute domains an open challenge in graph machine learning. 
In this paper, we propose STAGE, which encodes *statistical dependencies* between attributes rather than individual attribute values, which may differ in test graphs. By assuming these dependencies remain invariant under changes in node attributes, STAGE achieves provable generalization guarantees for a family of domain shifts. Empirically, STAGE demonstrates strong zero-shot performance on medium-sized datasets: when trained on multiple graph datasets with different attribute spaces (varying in types and number) and evaluated on graphs with entirely new attributes, STAGE achieves a relative improvement in Hits@1 between 40% to 103% in link prediction and a 10% improvement in node classification compared to state-of-the-art baselines.
Synthetic Text Generation for Training Large Language Models via Gradient Matching
Authors: Dang Nguyen; Zeman Li; Mohammadhossein Bateni; Vahab Mirrokni; Meisam Razaviyayn; Baharan Mirzasoleiman
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Synthetic data has the potential to improve the performance, training efficiency, and privacy of real training examples. Nevertheless, existing approaches for synthetic text generation are mostly heuristics and cannot generate human-readable text without compromising the privacy of real data, or provide performance guarantees for training Large Language Models (LLMs). In this work, we propose the first theoretically rigorous approach for generating synthetic human-readable text that provides convergence, performance, and privacy guarantees for fine-tuning LLMs on a target task. To do so, we leverage Alternating Direction Method of Multipliers (ADMM) that iteratively optimizes the embeddings of synthetic examples to match the noisy gradient of the target training or validation data, and maps them to a sequence of text tokens with low perplexity. In doing so, the generated synthetic text guarantees convergence of the model to a close neighborhood of the solution obtained by fine-tuning on real data and preserves their privacy. Experiments on various classification tasks confirm the effectiveness of our proposed approach. Our code is available at [https://github.com/BigML-CS-UCLA/GRADMM](https://github.com/BigML-CS-UCLA/GRADMM).
Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise
Authors: Ilias Diakonikolas; Mingchen Ma; Lisheng Ren; Christos Tzamos
Venue: ICML 2025 (ICML 2025 oral)

Abstract: We study the task of Multiclass Linear Classification (MLC) 
in the distribution-free PAC model 
with Random Classification Noise (RCN). 
Specifically, the learner is given a set of 
labeled examples $(x, y)$, where $x$ is drawn 
from an unknown distribution on $R^d$ 
and the labels are generated by a 
multiclass linear classifier corrupted with RCN. 
That is, the label $y$ is flipped from $i$ to $j$ 
with probability $H_{ij}$ 
according to a known noise matrix $H$ with 
non-negative separation 
$\sigma: = \min_{i \neq j} H_{ii}-H_{ij}$. 
The goal is to compute a hypothesis with 
small 0-1 error. For the special case of two labels, 
prior work has given polynomial-time algorithms 
achieving the optimal error. 
Surprisingly, little is known about 
the complexity of this task even for three labels.
As our main contribution, we show that the complexity 
of MLC with RCN becomes drastically different 
in the presence of three or more labels. 
Specifically, we prove super-polynomial
Statistical Query (SQ) lower bounds for this problem. 
In more detail, even for three labels and 
constant separation, 
we give a super-polynomial lower bound 
on the complexity of any SQ algorithm achieving optimal error. 
For a larger number of labels  and smaller separation, 
we show a super-polynomial SQ lower bound even 
for the weaker goal of achieving any constant factor approximation to the optimal loss or even beating the trivial hypothesis.
Do Bayesian Neural Networks Actually Behave Like Bayesian Models?
Authors: Gábor Pituk; Vik Shirvaikar; Tom Rainforth
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We empirically investigate how well popular approximate inference algorithms for Bayesian Neural Networks (BNNs) respect the theoretical properties of Bayesian belief updating. We find strong evidence on synthetic regression and real-world image classification tasks that common BNN algorithms such as variational inference, Laplace approximation, SWAG, and SGLD fail to update in a consistent manner, forget about old data under sequential updates, and violate the predictive coherence properties that would be expected of Bayesian methods. These observed behaviors imply that care should be taken when treating BNNs as true Bayesian models, particularly when using them beyond static prediction settings, such as for active, continual, or transfer learning.
AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation
Authors: Prashanth Vijayaraghavan; Luyao Shi; Ehsan Degan; Vandana Mukherjee; Xin Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Analog circuit topology synthesis is integral to Electronic Design Automation (EDA), enabling the automated creation of circuit structures tailored to specific design requirements. However, the vast design search space and strict constraint adherence make efficient synthesis challenging. Leveraging the versatility of Large Language Models (LLMs), we propose AUTOCIRCUIT-RL, a novel reinforcement learning (RL)-based framework for automated analog circuit synthesis. The framework operates in two phases: instruction tuning, where an LLM learns to generate circuit topologies from structured prompts encoding design constraints, and RL refinement, which further improves the instruction-tuned model using reward models that evaluate validity, efficiency, and output voltage. The refined model is then used directly to generate topologies that satisfy the design constraints. Empirical results show that AUTOCIRCUIT-RL generates ~12% more valid circuits and improves efficiency by ~14% compared to the best baselines, while reducing duplicate generation rates by ~38%. It achieves over 60% success in synthesizing valid circuits with limited training data, demonstrating strong generalization. These findings highlight the framework's effectiveness in scaling to complex circuits while maintaining efficiency and constraint adherence, marking a significant advancement in AI-driven circuit design.
What If We Recaption Billions of Web Images with LLaMA-3?
Authors: Xianhang Li; Haoqin Tu; Mude Hui; Zeyu Wang; Bingchen Zhao; Junfei Xiao; Sucheng Ren; Jieru Mei; Qing Liu; Huangjie Zheng; Yuyin Zhou; Cihang Xie
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Web-crawled image-text pairs are inherently noisy. Prior studies demonstrate that semantically aligning and enriching textual descriptions of these pairs can significantly enhance model training across various vision-language tasks, particularly text-to-image generation. However, large-scale investigations in this area remain predominantly closed-source. Our paper aims to bridge this community effort, leveraging the powerful and $\textit{open-sourced}$ LLaMA-3, a GPT-4 level LLM. Our recaptioning pipeline is simple: first, we fine-tune a LLaMA-3-8B powered LLaVA-1.5 and then employ it to recaption ~1.3 billion images from the DataComp-1B dataset. Our empirical results confirm that this enhanced dataset, Recap-DataComp-1B, offers substantial benefits in training advanced vision-language models. For discriminative models like CLIP, we observe an average of 3.1% enhanced zero-shot performance cross four cross-modal retrieval tasks using a mixed set of the original and our captions. For generative models like text-to-image Diffusion Transformers, the generated images exhibit a significant improvement in alignment with users' text instructions, especially in following complex queries. Our project page is https://www.haqtu.me/Recap-Datacomp-1B/.
A Bregman Proximal Viewpoint on Neural Operators
Authors: Abdel-Rahim Mezidi; Jordan Patracone; Saverio Salzo; Amaury Habrard; Massimiliano Pontil; Rémi Emonet; Marc Sebban
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present several advances on neural operators by viewing the action of operator layers as the minimizers of Bregman regularized optimization problems over Banach function spaces. The proposed framework allows interpreting the activation operators as Bregman proximity operators from dual to primal space. This novel viewpoint is general enough to recover classical neural operators as well as a new variant, coined Bregman neural operators, which includes the inverse activation operator and features the same expressivity of standard neural operators. Numerical experiments support the added benefits of the Bregman variant of Fourier neural operators for training deeper and more accurate models.
On the Robustness of Reward Models for Language Model Alignment
Authors: Jiwoo Hong; Noah Lee; Eunki Kim; Guijin Son; Woojin Chung; Aman Gupta; Shao Tang; James Thorne
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The Bradley-Terry (BT) model is widely practiced in reward modeling for reinforcement learning with human feedback (RLHF). Despite its effectiveness, reward models (RMs) trained with BT model loss as one-way classifiers are prone to over-optimization, losing generalizability to unseen inputs. In this paper, we study the cause of over-optimization and its downstream effects on the RLHF procedure, highlighting the importance of robustness in RMs. First, we show that the excessive dispersion of hidden state norms is the main source of over-optimization. Correspondingly, we propose batch-wise sum-to-zero regularization (BSR) that enforces reward sum for each batch to be zero-centered, constraining the rewards with abnormally large magnitudes. We assess the impact of BSR in improving robustness in RMs through four scenarios of over-optimization, where BSR consistently manifests better robustness on unseen inputs. Then, we compare the plain BT model and BSR on RLHF training and empirically show that robust RMs better align the policy to the gold preference model. Finally, we apply BSR to high-quality data and models, which surpasses state-of-the-art RMs in the 8B scale by adding more than 5\% in complex preference prediction tasks. By conducting RLOO training with 8B RMs, AlpacaEval 2.0, with reducing generation length by 40\% while adding a 7\% increase in win rate, further highlights that robustness in RMs induces robustness in RLHF training.
From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications
Authors: AJAY KUMAR JAISWAL; Yifan Wang; Lu Yin; Shiwei Liu; Runjin Chen; Jiawei Zhao; Ananth Grama; Yuandong Tian; Zhangyang Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large Language Models (LLMs) matrices can often be expressed in low-rank format with potential to relax memory and compute resource requirements. Unlike previous works which pivot around developing novel matrix decomposition algorithms, in this work we focus to study the emerging non-uniform low-rank properties across weight matrices in LLMs through the lens of stabilizing gradient subspace. \textit{Firstly,} we provide a theoretical framework to understand the stabilization of gradient subspaces through Hessian analysis. \textit{Secondly,} we empirically establish a consequential relationship between the gradient dynamics and low-rank expressiveness of weight matrices. Our findings reveal that different LLM components exhibit varying levels of converged low-rank structure, necessitating a non-uniform rank reduction across them to minimize performance drop due to compression. In view of that, we present \textit{Weight Low-Rank Projection} \textbf{(WeLore)} that unifies weight compression and memory-efficient fine-tuning as ONE, in a data-agnostic and one-shot way. Going beyond only as a compression technique, WeLore categorizes weight matrices into Low-rank Components (LRCs) and Non-Low-rank Components (N-LRCs) based on their ability to express themselves as low-rank. Our gradient dynamics perspective illustrate that \textit{LRCs tend to have better finetuning capabilities} and their standalone finetuning can closely mimic (sometimes outperform) the training loss trajectory and performance of full-finetuning with notable memory and compute footprint reduction. All codes and checkpoints will be released.
QUTE: Quantifying Uncertainty in TinyML models with Early-exit-assisted ensembles for model-monitoring
Authors: Nikhil Pratap Ghanathe; Steven J E Wilton
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Uncertainty quantification (UQ) provides a resource-efficient solution for on-device monitoring of tinyML models deployed remotely without access to true labels. However, existing UQ methods impose significant memory and compute demands, making them impractical for ultra-low-power, KB-sized tinyML devices. Prior work has attempted to reduce overhead by using early-exit ensembles to quantify uncertainty in a single forward pass, but these approaches still carry prohibitive costs. To address this, we propose QUTE, a novel resource-efficient early-exit-assisted ensemble architecture optimized for tinyML models. QUTE introduces additional output blocks at the final exit of the base network, distilling early-exit knowledge into these blocks to form a diverse yet lightweight ensemble. We show that QUTE delivers superior uncertainty quality on tiny models, achieving comparable performance on larger models with 59% smaller model sizes than the closest prior work. When deployed on a microcontroller, QUTE demonstrates a 31% reduction in latency on average. In addition, we show that QUTE excels at detecting accuracy-drop events, outperforming all prior works.
Diverging Preferences: When do Annotators Disagree and do Models Know?
Authors: Michael JQ Zhang; Zhilin Wang; Jena D. Hwang; Yi Dong; Olivier Delalleau; Yejin Choi; Eunsol Choi; Xiang Ren; Valentina Pyatkin
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We examine diverging preferences in human-labeled preference datasets. We develop a taxonomy of disagreement sources spanning ten categories across four high-level classes and find that the majority of disagreements are due to factors such as task underspecification or response style. Our findings challenge a standard assumption in reward modeling methods that annotator disagreements can be attributed to simple noise. We then explore how these findings impact two areas of LLM development: reward modeling training and evaluation. In our experiments, we demonstrate how standard reward modeling (e.g., Bradley-Terry) and LLM-as-Judge evaluation methods fail to account for divergence between annotators. These findings highlight challenges in LLM evaluations, which are greatly influenced by divisive features like response style, and in developing pluralistically aligned LLMs. To address these issues, we develop methods for identifying diverging preferences to mitigate their influence in evaluations and during LLM training.
FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems
Authors: Arya Fayyazi; Mehdi Kamal; Massoud Pedram
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We propose FACTER, a fairness-aware framework for LLM-based recommendation systems that integrates conformal prediction with dynamic prompt engineering. By introducing an adaptive semantic variance threshold and a violation-triggered mechanism, FACTER automatically tightens fairness constraints whenever biased patterns emerge. We further develop an adversarial prompt generator that leverages historical violations to reduce repeated demographic biases without retraining the LLM. Empirical results on MovieLens and Amazon show that FACTER substantially reduces fairness violations (up to 95.5%)  while maintaining strong recommendation accuracy, revealing semantic variance as a potent proxy of bias.
Adaptive Partitioning Schemes for Optimistic Optimization
Authors: Raja Sunkara; Ardhendu Tripathy
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Applications such as engineering design often require us to optimize a black-box function, i.e., a system whose inner processing is not analytically known and whose gradients are not available. Practitioners often have a fixed budget for the number of function evaluations and the performance of an optimization algorithm is measured by its simple regret. In this paper, we study the class of ``Optimistic Optimization'' algorithms for black-box optimization that use a partitioning scheme for the domain. We develop algorithms that learn a good partitioning scheme and use flexible surrogate models such as neural networks in the optimization procedure. For multi-index functions on an $m$-dimensional subspace within $d$ dimensions, our algorithm attains $\tilde{O}(n^{-\beta / d})$ regret, where $\beta = 1 + \frac{d-m}{2m-1}$, as opposed to $\tilde{O}(n^{-1/d})$ for SequOOL, a state-of-the-art optimistic optimization algorithm. Our approach is competitive across a wide range of numerical benchmarks. Additionally, we introduce weight quantization in a large language model as a novel task for black-box optimization. Our approach improves the quality of Activation-aware Weight Quantization (AWQ) of the OPT-1.3B model, achieving an approximate 10\% improvement in performance relative to the best possible unquantized model.
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models
Authors: Farzad Farhadzadeh; Debasmit Das; Shubhankar Borse; Fatih Porikli
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce ProLoRA, enabling zero-shot adaptation of parameter-efficient fine-tuning in text-to-image diffusion models. ProLoRA transfers pre-trained low-rank adjustments (e.g., LoRA) from a source to a target model without additional training data. This overcomes the limitations of traditional methods that require retraining when switching base models, often challenging due to data constraints. ProLoRA achieves this via projection of source adjustments into the target model's weight space, leveraging subspace and null space similarities and selectively targeting aligned layers. Evaluations on established text-to-image models demonstrate successful knowledge transfer and comparable performance without retraining.
Self-Supervised Transformers as Iterative Solution Improvers for Constraint Satisfaction
Authors: Yudong Xu; Wenhao Li; Scott Sanner; Elias Boutros Khalil
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present a Transformer-based framework for Constraint Satisfaction Problems (CSPs). 
CSPs find use in many applications and thus accelerating their solution with machine learning is of wide interest. Most existing approaches rely on supervised learning from feasible solutions or reinforcement learning, paradigms that require either feasible solutions to these NP-Complete CSPs or large training budgets and a complex expert-designed reward signal. To address these challenges, we propose ConsFormer, a self-supervised framework that leverages a Transformer as a solution refiner. ConsFormer constructs a solution to a CSP iteratively in a process that mimics local search. Instead of using feasible solutions as labeled data, we devise differentiable approximations to the discrete constraints of a CSP to guide model training. Our model is trained to improve random assignments for a single step but is deployed iteratively at test time, circumventing the bottlenecks of supervised and reinforcement learning. Experiments on Sudoku, Graph Coloring, Nurse Rostering, and MAXCUT demonstrate that our method can tackle out-of-distribution CSPs simply through additional iterations.