ICML 2024

2,610 papers

Generalized Smooth Variational Inequalities: Methods with Adaptive Stepsizes

Daniil Vankov, Angelia Nedich, Lalitha Sankar

Forget Sharpness: Perturbed Forgetting of Model Biases Within SAM Dynamics

Ankit Vani, Frederick Tung, Gabriel L. Oliveira, Hossein Sharifi-Noghabi

When Representations Align: Universality in Representation Learning Dynamics

Loek Van Rossem, Andrew M Saxe

On the Independence Assumption in Neurosymbolic Learning

Emile van Krieken, Pasquale Minervini, Edoardo Ponti, Antonio Vergari

Proactive DP: A Multiple Target Optimization Framework for DP-SGD

Marten van Dijk, Nhuong Van Nguyen, Toan N. Nguyen, Lam M. Nguyen, PHUONG HA NGUYEN

Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models

Jan Van Den Brand, Zhao Song, Tianyi Zhou

Piecewise Constant and Linear Regression Trees: An Optimal Dynamic Programming Approach

Mim Van Den Bos, Jacobus G. M. van der Linden, Emir Demirović

Position: Why Tabular Foundation Models Should Be a Research Priority

Boris van Breugel, Mihaela van der Schaar

Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition

Michael Valancius, Maxwell Lennon, Junier Oliva

Reward-Free Kernel-Based Reinforcement Learning

Sattar Vakili, Farhang Nabiei, Da-shan Shiu, Alberto Bernacchia

Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function

Keyon Vafa, Ashesh Rambachan, Sendhil Mullainathan

Feedback Efficient Online Fine-Tuning of Diffusion Models

Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia

Improving Antibody Humanness Prediction using Patent Data

Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe

Matroid Semi-Bandits in Sublinear Time

Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu

ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data

Carmen Martin Turrero, Maxence Bouvier, Manuel Breitenstein, Pietro Zanuttigh, Vincent Parret

An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems

Hitesh Tulsiani, David Chan, Shalini Ghosh, Garima Lalwani, Prabhat Pandey

Coactive Learning for Large Language Models using Implicit User Feedback

Aaron David Tucker, Kianté Brantley, Adam Cahall, Thorsten Joachims

An Empirical Study Into What Matters for Calibrating Vision-Language Models

Weijie Tu, Weijian Deng, Dylan Campbell, Stephen Gould, Tom Gedeon

Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring

Taira Tsuchiya, Shinji Ito, Junya Honda

Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data

Nikita Tsoy, Nikola Konstantinov

Learning Optimal Projection for Forecast Reconciliation of Hierarchical Time Series

Asterios Tsiourvas, Wei Sun, Georgia Perakis, Pin-Yu Chen, Yada Zhu

Overcoming the Optimizer’s Curse: Obtaining Realistic Prescriptions from Neural Networks

Asterios Tsiourvas, Georgia Perakis

Predictive Linear Online Tracking for Unknown Targets

Anastasios Tsiamis, Aren Karapetyan, Yueshan Li, Efe C. Balta, John Lygeros

QuIP$#$: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks

Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa

Editing Partially Observable Networks via Graph Diffusion Models

Puja Trivedi, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt

Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs

Stelios Triantafyllou, Aleksa Sukovic, Debmalya Mandal, Goran Radanovic

Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments

Allen Tran, Aurelien Bibaut, Nathan Kallus

Stereographic Spherical Sliced Wasserstein Distances

Huy Tran, Yikun Bai, Abihith Kothapalli, Ashkan Shahbazi, Xinran Liu

Causal Effect Identification in LiNGAM Models with Latent Confounders

Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash

Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Florian Tramer, Gautam Kamath, Nicholas Carlini

Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics

Artur Toshev, Jonas A. Erbesdobler, Nikolaus A. Adams, Johannes Brandstetter

An Analysis of Linear Time Series Forecasting Models

William Toner, Luke Nicholas Darlow

Position: Do Not Explain Vision Models Without Context

Paulina Tomaszewska, Przemyslaw Biecek

How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model

Umberto Maria Tomasini, Matthieu Wyart

Position: Enforced Amnesia as a Way to Mitigate the Potential Risk of Silent Suffering in the Conscious AI

Yegor Tkachenko

Faster Maximum Inner Product Search in High Dimensions

Mo Tiwari, Ryan Kang, Jaeyong Lee, Donghyun Lee, Christopher J Piech

FRAPPÉ: A Group Fairness Framework for Post-Processing Everything

Alexandru Tifrea, Preethi Lahoti, Ben Packer, Yoni Halpern, Ahmad Beirami

Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints

Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt, Payel Das

Copula-Nested Spectral Kernel Network

Jinyue Tian, hui xue, Yanfang Xue, Pengfei Fang

Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms

Ye Tian, Haolei Weng, Yang Feng

Ranking-based Client Imitation Selection for Efficient Federated Learning

Chunlin Tian, Zhan Shi, Xinpeng Qin, Li Li, Cheng-Zhong Xu

Liouville Flow Importance Sampler

Yifeng Tian, Nishant Panda, Yen Ting Lin

MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence

Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung

Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval

Qiwei Tian, Chenhao Lin, Zhengyu Zhao, Qian Li, Chao Shen

Risk Estimation in a Markov Cost Process: Lower and Upper Bounds

Gugan Thoppe, Prashanth L A, Sanjay P. Bhat

Beyond Individual Input for Deep Anomaly Detection on Tabular Data

Hugo Thimonier, Fabrice Popineau, Arpad Rimmel, Bich-Lien Doan

A New Branch-and-Bound Pruning Framework for ℓ_0-Regularized Problems

Theo Guyard, Cedric Herzet, Clément Elvira, Ayse-Nur Arslan

Position: What makes an image realistic?

Lucas Theis

Bayesian Adaptation of Network Depth and Width for Continual Learning

Jeevan Thapa, Rui Li

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

Rahul Thapa, Bryan He, Magnus Ruud Kjaer, Hyatt Moore Iv, Gauri Ganjoo

Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency

Vithursan Thangarasa, Shreyas Saxena, Abhay Gupta, Sean Lie

Quantum Positional Encodings for Graph Neural Networks

Slimane Thabet, Mehdi Djellabi, Igor Olegovich Sokolov, Sachin Kasture, Louis-Paul Henry

Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup

Damien Teney, Jindong Wang, Ehsan Abbasnejad

Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method

Qinghua Tao, Francesco Tonin, Alex Lambert, Yingyi Chen, Panagiotis Patrinos

MLI Formula: A Nearly Scale-Invariant Solution with Noise Perturbation

Bowen Tao, Xin-Chun Li, De-Chuan Zhan

QUEST: Query-Aware Sparsity for Efficient Long-Context LLM Inference

Jiaming Tang, Yilong Zhao, Kan Zhu, Guangxuan Xiao, Baris Kasikci

MathScale: Scaling Instruction Tuning for Mathematical Reasoning

Zhengyang Tang, Xingxing Zhang, Benyou Wang, Furu Wei

Finite Smoothing Algorithm for High-Dimensional Support Vector Machines and Quantile Regression

Qian Tang, Yikai Zhang, Boxiang Wang

SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification

Yehui Tang, Nianzu Yang, Mabiao Long, Junchi Yan

StrokeNUWA—Tokenizing Strokes for Vector Graphic Synthesis

Zecheng Tang, Chenfei Wu, Zekai Zhang, Minheng Ni, Shengming Yin

Membership Inference Attacks on Diffusion Models via Quantile Regression

Shuai Tang, Steven Wu, Sergul Aydore, Michael Kearns, Aaron Roth

Accelerating Parallel Sampling of Diffusion Models

Zhiwei Tang, Jiasheng Tang, Hao Luo, Fan Wang, Tsung-Hui Chang

Merging Multi-Task Models via Weight-Ensembling Mixture of Experts

Anke Tang, Li Shen, Yong Luo, Nan Yin, Lefei Zhang

Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration

Xiaole Tang, Xin Hu, Xiang Gu, Jian Sun

Rethinking Optimization and Architecture for Tiny Language Models

Yehui Tang, Kai Han, Fangcheng Liu, Yunsheng Ni, Yuchuan Tian

Generalized Preference Optimization: A Unified Approach to Offline Alignment

Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos

Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization

Xueyang Tang, Song Guo, Jingcai Guo, Jie Zhang, Yue Yu

Post-hoc Part-Prototype Networks

Andong Tan, Fengtao Zhou, Hao Chen

OTMatch: Improving Semi-Supervised Learning with Optimal Transport

Zhiquan Tan, Kaipeng Zheng, Weiran Huang

Information Flow in Self-Supervised Learning

Zhiquan Tan, Jingqin Yang, Weiran Huang, Yang Yuan, Yifan Zhang

Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem

Zhentao Tan, Yadong Mu

Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning

Hengkai Tan, Songming Liu, Kai Ma, Chengyang Ying, Xingxing Zhang

Community-Invariant Graph Contrastive Learning

Shiyin Tan, Dongyuan Li, Renhe Jiang, Ying Zhang, Manabu Okumura

Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective

Cheng Tan, Zhangyang Gao, Hanqun CAO, Xingran Chen, Ge Wang

Codebook Features: Sparse and Discrete Interpretability for Neural Networks

Alex Tamkin, Mohammad Taufeeque, Noah Goodman

Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds

Shion Takeno, Yu Inatsu, Masayuki Karasuyama, Ichiro Takeuchi

Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective

Shokichi Takakura, Taiji Suzuki

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data

Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider

A Universal Class of Sharpness-Aware Minimization Algorithms

Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet

Sample Complexity Bounds for Estimating Probability Divergences under Invariances

Behrooz Tahmasebi, Stefanie Jegelka

Fast Sampling-Based Sketches for Tensors

William Joseph Swartworth, David Woodruff

A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal

Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance

Jakub Svoboda, Suguman Bansal, Krishnendu Chatterjee

Interpretable Deep Clustering for Tabular Data

Jonathan Svirsky, Ofir Lindenbaum

On a Combinatorial Problem Arising in Machine Teaching

Joakim Sunde, Brigt Håvardstun, Jan Kratochvı́l, Jan Arne Telle

BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models

Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai

Self-cognitive Denoising in the Presence of Multiple Noisy Label Sources

Yi-Xuan Sun, Ya-Lin Zhang, Bin Han, Longfei Li, Jun Zhou

Learning Latent Dynamic Robust Representations for World Models

Ruixiang Sun, Hongyu Zang, Xin Li, Riashat Islam

Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding

Chuanhao Sun, Zhihang Yuan, Kai Xu, Luo Mai, Siddharth N

video-SALMONN: Speech-Enhanced Audio-Visual Large Language Models

Guangzhi Sun, Wenyi Yu, Changli Tang, Xianzhao Chen, Tian Tan

Regression Learning with Limited Observations of Multivariate Outcomes and Features

Yifan Sun, Grace Yi

FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models

Jingwei Sun, Ziyue Xu, Hongxu Yin, Dong Yang, Daguang Xu

Learning Graph Representation via Graph Entropy Maximization

Ziheng Sun, Xudong Wang, Chris Ding, Jicong Fan

Online Adaptive Anomaly Thresholding with Confidence Sequences

Sophia Huiwen Sun, Abishek Sankararaman, Balakrishnan Murali Narayanaswamy

LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering

Li Sun, Zhenhao Huang, Hao Peng, Yujie Wang, Chunyang Liu

Online Algorithms with Uncertainty-Quantified Predictions

Bo Sun, Jerry Huang, Nicolas Christianson, Mohammad Hajiesmaili, Adam Wierman

DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton

Yiyou Sun, Junjie Hu, Wei Cheng, Haifeng Chen

Constrained Reinforcement Learning Under Model Mismatch

Zhongchang Sun, Sihong He, Fei Miao, Shaofeng Zou

Representing Molecules as Random Walks Over Interpretable Grammars

Michael Sun, Minghao Guo, Weize Yuan, Veronika Thost, Crystal Elaine Owens

Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents

Chung-En Sun, Sicun Gao, Tsui-Wei Weng