Most Cited ICML "attention-based alignment" Papers

A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing

yu chen, Jing Lian, Zhaofei Yu et al.

An Error Analysis of Flow Matching for Deep Generative Modeling

Zhengyu Zhou, Weiwei Liu

AKORN: Adaptive Knots generated Online for RegressioN splines

Sunil Madhow, Dheeraj Baby, Yu-Xiang Wang

Latent Diffusion Planning for Imitation Learning

Amber Xie, Oleh Rybkin, Dorsa Sadigh et al.

Feature out! Let Raw Image as Your Condition for Blind Face Restoration

XINMIN QIU, Gege Chen, Bonan Li et al.

MixMin: Finding Data Mixtures via Convex Minimization

Anvith Thudi, Evianne Rovers, Yangjun Ruan et al.

Relational Conformal Prediction for Correlated Time Series

Andrea Cini, Alexander Jenkins, Danilo Mandic et al.

Heads up! Large Language Models Can Perform Tasks Without Your Instruction via Selective Attention Head Masking

Senyu Han, Hongchuan Zeng, Kai Yu et al.

GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction

Muleilan Pei, Shaoshuai Shi, Lu Zhang et al.

How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias

Ruiquan Huang, Yingbin LIANG, Jing Yang

Thinking LLMs: General Instruction Following with Thought Generation

Tianhao Wu, Janice Lan, Weizhe Yuan et al.

Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks

Angelica Chen, Samuel Stanton, Frances Ding et al.

Lightweight Protocols for Distributed Private Quantile Estimation

Anders Aamand, Fabrizio Boninsegna, Abigail Gentle et al.

MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Free Rectification

TianChi Xing, Bonan Li, Congying Han et al.

STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving

Kefan Dong, Tengyu Ma

PARM: Multi-Objective Test-Time Alignment via Preference-Aware Autoregressive Reward Model

Baijiong Lin, Weisen Jiang, Yuancheng Xu et al.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Chendi Ge, Xin Wang, Zeyang Zhang et al.

A Mathematical Framework for AI-Human Integration in Work

L. Elisa Celis, Lingxiao Huang, Nisheeth K. Vishnoi

DAMA: Data- and Model-aware Alignment of Multi-modal LLMs

Jinda Lu, Junkang Wu, Jinghan Li et al.

Position: AI Should Not Be An Imitation Game: Centaur Evaluations

Andreas Haupt, Erik Brynjolfsson

Position: LLMs Need a Bayesian Meta-Reasoning Framework for More Robust and Generalizable Reasoning

Hanqi Yan, Linhai Zhang, Jiazheng Li et al.

Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More

Feng Wang, Yaodong Yu, Wei Shao et al.

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data

Jingang QU, David Holzmüller, Gael Varoquaux et al.

Dimension-Free Adaptive Subgradient Methods with Frequent Directions

Sifan Yang, Yuanyu Wan, Peijia Li et al.

Elucidating the design space of language models for image generation

Xuantong Liu, Shaozhe Hao, Xianbiao Qi et al.

MODULI: Unlocking Preference Generalization via Diffusion Models for Offline Multi-Objective Reinforcement Learning

Yifu Yuan, Zhenrui Zheng, Zibin Dong et al.

R*: Efficient Reward Design via Reward Structure Evolution and Parameter Alignment Optimization with Large Language Models

Pengyi Li, Jianye Hao, Hongyao Tang et al.

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Position: Towards Unified Alignment Between Agents, Humans, and Environment

Zonghan Yang, an liu, Zijun Liu et al.

Fair Off-Policy Learning from Observational Data

Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel

Consistent Submodular Maximization

PAUL DUETTING, Federico Fusco, Silvio Lattanzi et al.

Automated Statistical Model Discovery with Language Models

Michael Li, Emily Fox, Noah Goodman

WARM: On the Benefits of Weight Averaged Reward Models

Alexandre Rame, Nino Vieillard, Léonard Hussenot et al.

MusicRL: Aligning Music Generation to Human Preferences

Geoffrey Cideron, Sertan Girgin, Mauro Verzetti et al.

Nash Learning from Human Feedback

REMI MUNOS, Michal Valko, Daniele Calandriello et al.

Kernel-Based Evaluation of Conditional Biological Sequence Models

Pierre Glaser, Steffan Paul, Alissa M. Hummer et al.

TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge

Young Kwon, Rui Li, Stylianos Venieris et al.

Learning Associative Memories with Gradient Descent

Vivien Cabannnes, Berfin Simsek, Alberto Bietti

Batch and match: black-box variational inference with a score-based divergence

Diana Cai, Chirag Modi, Loucas Pillaud-Vivien et al.

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts

Jianan Zhou, Zhiguang Cao, Yaoxin Wu et al.

Hybrid$^2$ Neural ODE Causal Modeling and an Application to Glycemic Response

Junyi Zou, Matthew Levine, Dessi Zaharieva et al.

Stereographic Spherical Sliced Wasserstein Distances

Huy Tran, Yikun Bai, Abihith Kothapalli et al.

How Learning by Reconstruction Produces Uninformative Features For Perception

Randall Balestriero, Yann LeCun

Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing

Youwei Shu, Xi Xiao, Derui Wang et al.

Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference

JIAN XU, Delu Zeng, John Paisley

Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners

Chengjie Wu, Hao Hu, yiqin yang et al.

The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective

Chi-Heng Lin, Chiraag Kaushik, Eva Dyer et al.

CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay

Natasha Butt, Blazej Manczak, Auke Wiggers et al.

Incorporating Information into Shapley Values: Reweighting via a Maximum Entropy Approach

Darya Biparva, Donatello Materassi

HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning

Shengchao Hu, Ziqing Fan, Li Shen et al.

Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization

Ziqing Fan, Shengchao Hu, Jiangchao Yao et al.

Multi-group Learning for Hierarchical Groups

Samuel Deng, Daniel Hsu

Ensemble Pruning for Out-of-distribution Generalization

Fengchun Qiao, Xi Peng

Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation

Kartik Sharma, Srijan Kumar, Rakshit Trivedi

On the Asymptotic Distribution of the Minimum Empirical Risk

Jacob Westerhout, TrungTin Nguyen, Xin Guo et al.

Exploiting Code Symmetries for Learning Program Semantics

Kexin Pei, Weichen Li, Qirui Jin et al.

Towards Compositionality in Concept Learning

Adam Stein, Aaditya Naik, Yinjun Wu et al.

A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Gokul Swamy, Christoph Dann, Rahul Kidambi et al.

Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models

Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman et al.

Image Hijacks: Adversarial Images can Control Generative Models at Runtime

Luke Bailey, Euan Ong, Stuart Russell et al.

TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision

Zhuo Chen, Jacob McCarran, Esteban Vizcaino et al.

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics

Noga Mudrik, Yenho Chen, Eva Yezerets et al.

Behavior Generation with Latent Actions

Seungjae Lee, Yibin Wang, Haritheja Etukuru et al.

Measures of diversity and space-filling designs for categorical data

AstraZeneca Pharmaceutica, Emilio Domínguez-Sánchez, Merwan Barlier et al.

Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains

Jiale Zhao, Wanru Zhuang, Jia Song et al.

QuRating: Selecting High-Quality Data for Training Language Models

Alexander Wettig, Aatmik Gupta, Saumya Malik et al.

On the Maximal Local Disparity of Fairness-Aware Classifiers

Jinqiu Jin, Haoxuan Li, Fuli Feng

Benchmarking Deletion Metrics with the Principled Explanations

Yipei Wang, Xiaoqian Wang

Benign Overfitting in Adversarial Training of Neural Networks

Yunjuan Wang, Kaibo Zhang, Raman Arora

Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee

George Chen

Sample as you Infer: Predictive Coding with Langevin Dynamics

Umais Zahid, Qinghai Guo, Zafeirios Fountas

APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference

Bowen Zhao, Hannaneh Hajishirzi, Qingqing Cao

Extracting Training Data From Document-Based VQA Models

Francesco Pinto, Nathalie Rauschmayr, Florian Tramer et al.

MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data

Paul Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva et al.

Scalable AI Safety via Doubly-Efficient Debate

Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras

GeoMFormer: A General Architecture for Geometric Molecular Representation Learning

Tianlang Chen, Shengjie Luo, Di He et al.

Minimizing $f$-Divergences by Interpolating Velocity Fields

Song Liu, Jiahao Yu, Jack Simons et al.

Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses

Panagiotis Koromilas, Giorgos Bouritsas, Theodoros Giannakopoulos et al.

Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs

Yeonhong Park, Jake Hyun, SangLyul Cho et al.

Robust Universal Adversarial Perturbations

Changming Xu, Gagandeep Singh

Low-Cost High-Power Membership Inference Attacks

Sajjad Zarifzadeh, Philippe Liu, Reza Shokri

Early Time Classification with Accumulated Accuracy Gap Control

Liran Ringel, Regev Cohen, Daniel Freedman et al.

Evaluating Instrument Validity using the Principle of Independent Mechanisms

Patrick F. Burauel

Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding

Guangyi Liu, Yu Wang, Zeyu Feng et al.

CogBench: a large language model walks into a psychology lab

Julian Coda-Forno, Marcel Binz, Jane Wang et al.

Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks

Akshay Kumar Jagadish, Julian Coda-Forno, Mirko Thalmann et al.

$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts

Guanjie Chen, Xinyu Zhao, Tianlong Chen et al.

Simple linear attention language models balance the recall-throughput tradeoff

Simran Arora, Sabri Eyuboglu, Michael Zhang et al.

Principled Preferential Bayesian Optimization

Wenjie Xu, Wenbin Wang, Yuning Jiang et al.

Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices

Nathaniel Cohen, Vladimir Kulikov, Matan Kleiner et al.

The Expressive Power of Path-Based Graph Neural Networks

Caterina Graziani, Tamara Drucks, Fabian Jogl et al.

Environment Design for Inverse Reinforcement Learning

Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis

Stable Differentiable Causal Discovery

Achille Nazaret, Justin Hong, Elham Azizi et al.

Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective

Wu Lin, Felix Dangel, Runa Eschenhagen et al.

Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning

Saber Malekmohammadi, Yaoliang Yu, YANG CAO

Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning

Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong et al.

From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble

Qianlong Wen, Mingxuan Ju, Zhongyu Ouyang et al.

Sparser, Better, Deeper, Stronger: Improving Static Sparse Training with Exact Orthogonal Initialization

Aleksandra I. Nowak, Łukasz Gniecki, Filip Szatkowski et al.

TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression

Megh Shukla, Mathieu Salzmann, Alexandre Alahi

Challenges in Training PINNs: A Loss Landscape Perspective

Pratik Rathore, Weimu Lei, Zachary Frangella et al.

Position: The Causal Revolution Needs Scientific Pragmatism

Joshua Loftus

Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms

Yichen Li, Chicheng Zhang

Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation

Michelle Pan, Mariah Schrum, Vivek Myers et al.

Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)

Drew Prinster, Samuel Stanton, Anqi Liu et al.

Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent

Yingru Li, Jiawei Xu, Lei Han et al.

SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals

Rahul Thapa, Bryan He, Magnus Ruud Kjaer et al.

Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation

Shiyang Lai, Yujin Potter, Junsol Kim et al.

Revisiting Context Aggregation for Image Matting

Qinglin Liu, Xiaoqian Lv, Quanling Meng et al.

Learning Scale-Aware Spatio-temporal Implicit Representation for Event-based Motion Deblurring

Wei Yu, Jianing Li, Shengping Zhang et al.

Mind the Boundary: Coreset Selection via Reconstructing the Decision Boundary

Shuo Yang, Zhe Cao, Sheng Guo et al.

Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of AI

Daniel McDuff, Tim Korjakow, Scott Cambo et al.

Infinite-Horizon Distributionally Robust Regret-Optimal Control

Taylan Kargin, Joudi Hajar, Vikrant Malik et al.

Accelerating Heterogeneous Federated Learning with Closed-form Classifiers

Eros Fanì, Raffaello Camoriano, Barbara Caputo et al.

Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition

Mohamad Amin Mohamadi, Zhiyuan Li, Lei Wu et al.

Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning

Kakei Yamamoto, Kazusato Oko, Zhuoran Yang et al.

Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model

Mikail Khona, Maya Okawa, Jan Hula et al.

Neural-Kernel Conditional Mean Embeddings

Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic

Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning

Michal Nauman, Michał Bortkiewicz, Piotr Milos et al.

Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE

Hao Wu, Huiyuan Wang, kun wang et al.

Improving Adversarial Energy-Based Model via Diffusion Process

Cong Geng, Tian Han, Peng-Tao Jiang et al.

Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning

Donghu Kim, Hojoon Lee, Kyungmin Lee et al.

Learning from Streaming Data when Users Choose

Jinyan Su, Sarah Dean

Conformal Prediction with Learned Features

Shayan Kiyani, George J. Pappas, Hamed Hassani

Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling

Mingze Wang, Zeping Min, Lei Wu

T-Cal: An Optimal Test for the Calibration of Predictive Models

Donghwan Lee, Xinmeng Huang, Hamed Hassani et al.

Watermark Stealing in Large Language Models

Nikola Jovanović, Robin Staab, Martin Vechev

Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching

Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann et al.

Fair Data Representation for Machine Learning at the Pareto Frontier

Shizhou Xu, Thomas Strohmer

AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers

Reduan Achtibat, Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer et al.

BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images

Sandesh Adhikary, Anqi Li, Byron Boots

Probabilistic Generating Circuits - Demystified

Sanyam Agarwal, Markus Bläser

The Non-linear $F$-Design and Applications to Interactive Learning

Alekh Agarwal, Jian Qian, Alexander Rakhlin et al.

Distinguishing the Knowable from the Unknowable with Language Models

Gustaf Ahdritz, Tian Qin, Nikhil Vyas et al.

Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise

Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook et al.

Triple Changes Estimator for Targeted Policies

Sina Akbari, Negar Kiyavash

Learning Mixtures of Gaussian Processes through Random Projection

Emmanuel Akeweje, Mimi Zhang

Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity

Ahmet Alacaoglu, Donghwan Kim, Stephen Wright

Physics of Language Models: Part 3.1, Knowledge Storage and Extraction

Zeyuan Allen-Zhu, Yuanzhi Li

Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates

Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui et al.

The Privacy Power of Correlated Noise in Decentralized Learning

Youssef Allouah, Anastasiia Koloskova, Aymane Firdoussi et al.

Position: Stop Making Unscientific AGI Performance Claims

Patrick Altmeyer, Andrew Demetriou, Antony Bartlett et al.

Robust Graph Matching when Nodes are Corrupt

Taha Ameen Ur Rahman, Bruce Hajek

A Rate-Distortion View of Uncertainty Quantification

Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen et al.

Practical Performance Guarantees for Pipelined DNN Inference

Aaron Archer, Matthew Fahrbach, Kuikui Liu et al.

An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation

Jonas Arruda, Yannik Schälte, Clemens Peiter et al.

Learning the Target Network in Function Space

Kavosh Asadi, Yao Liu, Shoham Sabach et al.

Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation

Hugo Attali, Davide Buscaldi, Nathalie Pernelle

Random features models: a way to study the success of naive imputation

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut et al.

Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance

Mingyuan Bai, Wei Huang, Li Tenghui et al.

Memory Consolidation Enables Long-Context Video Understanding

Ivana Balazevic, Yuge Shi, Pinelopi Papalampidi et al.

Analyzing $D^\alpha$ seeding for $k$-means

Etienne Bamas, Sai Ganesh Nagarajan, Ola Svensson

Relational DNN Verification With Cross Executional Bound Refinement

Debangshu Banerjee, Gagandeep Singh

Scale-Free Image Keypoints Using Differentiable Persistent Homology

Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno et al.

Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies

Brian Bartoldson, James Diffenderfer, Konstantinos Parasyris et al.

Neural Diffusion Models

Grigory Bartosh, Dmitry Vetrov, Christian Andersson Naesseth

Generalization in Kernel Regression Under Realistic Assumptions

Daniel Barzilai, Ohad Shamir

On Mechanistic Knowledge Localization in Text-to-Image Generative Models

Samyadeep Basu, Keivan Rezaei, Priyatham Kattakinda et al.

Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions

Yongqiang Cai

Refining Minimax Regret for Unsupervised Environment Design

Michael Beukman, Samuel Coward, Michael Matthews et al.

Position: Scaling Simulation is Neither Necessary Nor Sufficient for In-the-Wild Robot Manipulation

Homanga Bharadhwaj

Multi-Patch Prediction: Adapting Language Models for Time Series Representation Learning

Yuxuan Bian, Xuan Ju, Jiangtong Li et al.

Improving fine-grained understanding in image-text pre-training

Ioana Bica, Anastasija Ilic, Matthias Bauer et al.

Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering

Mitchell Black, Lucy Lin, Weng-Keen Wong et al.

Stability Evaluation through Distributional Perturbation Analysis

Jose Blanchet, Peng Cui, Jiajin Li et al.

Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling

Denis Blessing, Xiaogang Jia, Johannes Esslinger et al.

How Spurious Features are Memorized: Precise Analysis for Random and NTK Features

Simone Bombari, Marco Mondelli

Position: Machine Learning-powered Assessments of the EU Digital Services Act Aid Quantify Policy Impacts on Online Harms

Eleonora Bonel, Luca Nannini, Davide Bassi et al.

Random matrix theory improved Fréchet mean of symmetric positive definite matrices

Florent Bouchard, Ammar Mian, Malik TIOMOKO et al.

Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees

Marco Bressan, Mauro Sozio

Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formula

Kirill Brilliantov, Fedor Pavutnitskiy, Dmitrii A. Pasechniuk et al.

Langevin Policy for Safe Reinforcement Learning

Fenghao Lei, Long Yang, Shiting Wen et al.

Semantically-correlated memories in a dense associative model

Thomas F Burns

Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design

Andrew Campbell, Jason Yim, Regina Barzilay et al.

Successor Features for Efficient Multi-Subject Controlled Text Generation

Meng Cao, Mehdi Fatemi, Jackie Chi Kit Cheung et al.

Limited Preference Aided Imitation Learning from Imperfect Demonstrations

Xingchen Cao, Fan-Ming Luo, Junyin Ye et al.

Can a Few Decide for Many? The Metric Distortion of Sortition

Ioannis Caragiannis, Evi Micha, Jannik Peters

On the Implicit Bias of Adam

Matias Cattaneo, Jason Klusowski, Boris Shigida

Feasibility Consistent Representation Learning for Safe Reinforcement Learning

Zhepeng Cen, Yihang Yao, Zuxin Liu et al.

Feature Importance Disparities for Data Bias Investigations

Peter Chang, Leor Fishman, Seth Neel

MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion

Di Chang, Yichun Shi, Quankai Gao et al.

Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning

Weilin Chen, Ruichu Cai, Zeqin Yang et al.

InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models

Lichang Chen, Jiuhai Chen, Tom Goldstein et al.

MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective

Yizhuo Chen, Chun-Fu (Richard) Chen, Hsiang Hsu et al.

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Zixiang Chen, Yihe Deng, Huizhuo Yuan et al.

Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components

Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low

Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise

Xi Chen, Zhewen Hou, Christopher Metzler et al.

Accelerated Policy Gradient for s-rectangular Robust MDPs with Large State Spaces

Ziyi Chen, Heng Huang

Offline Transition Modeling via Contrastive Energy Learning

Ruifeng Chen, Chengxing Jia, Zefang Huang et al.

Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank

Mouxiang Chen, Chenghao Liu, Zemin Liu et al.

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Yihang Chen, Fanghui Liu, Taiji Suzuki et al.

DiJiang: Efficient Large Language Models through Compact Kernelization

Hanting Chen, Liuzhicheng Liuzhicheng, Xutao Wang et al.

MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models

Justin Chih-Yao Chen, Swarnadeep Saha, Elias Stengel-Eskin et al.

CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process

Guangyi Chen, Yifan Shen, Zhenhao Chen et al.

GRATH: Gradual Self-Truthifying for Large Language Models

Weixin Chen, Dawn Song, Bo Li

Performative Prediction with Bandit Feedback: Learning through Reparameterization

Yatong Chen, Wei Tang, Chien-Ju Ho et al.

Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes

Yingyi Chen, Qinghua Tao, Francesco Tonin et al.

Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning

Junfeng CHEN, Kailiang Wu

LLaGA: Large Language and Graph Assistant

Runjin Chen, Tong Zhao, Ajay Jaiswal et al.

Compact Optimality Verification for Optimization Proxies

Wenbo Chen, Haoruo Zhao, Mathieu Tanneau et al.

Enhancing Implicit Shape Generators Using Topological Regularizations

Liyan Chen, Yan Zheng, Yang Li et al.