ICML 2024

2,610 papers

Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments

Antoine Dedieu, Wolfgang Lehrach, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla

Provably Better Explanations with Optimized Aggregation of Feature Attributions

Thomas Decker, Ananta R. Bhattarai, Jindong Gu, Volker Tresp, Florian Buettner

Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods

Riccardo De Santi, Manish Prajapat, Andreas Krause

Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction

Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause

New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering

Sanjoy Dasgupta, Eduardo Sany Laber

Disparate Impact on Group Accuracy of Linearization for Private Inference

Saswat Das, Marco Romanelli, Ferdinando Fioretto

A decoder-only foundation model for time-series forecasting

Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou

Understanding the Training Speedup from Sampling with Approximate Losses

Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal, Sujay Sanghavi

Larimar: Large Language Models with Episodic Memory Control

Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarathkrishna Swaminathan

Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data

Giannis Daras, Alex Dimakis, Constantinos Costis Daskalakis

Boosting Offline Optimizers with Surrogate Sensitivity

Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Tri Dao, Albert Gu

Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Features Model

Hien Dang, Tho Tran Huu, Tan Minh Nguyen, nhật Hồ

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

Yatin Dandi, Emanuele Troiani, Luca Arnaboldi, Luca Pesce, Lenka Zdeborová

Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold

Tingting Dan, Ziquan Wei, Won Hwa Kim, Guorong Wu

Physics and Lie symmetry informed Gaussian processes

David Dalton, Dirk Husmeier, Hao Gao

Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models

Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrovic

Averaging n-step Returns Reduces Variance in Reinforcement Learning

Brett Daley, Martha White, Marlos C. Machado

Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation

Juntao Dai, Yaodong Yang, Qian Zheng, Gang Pan

High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion

Yu Dai, Junchen Shen, Zijie Zhai, Danlin Liu, Jingyang Chen

Multi-View Clustering by Inter-cluster Connectivity Guided Reward

Hao Dai, Yang Liu, Peng Su, Hecheng Cai, Shudong Huang

Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis

Jessica Dai

Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training

Tehila Dahan, Kfir Yehuda Levy

Getting the most out of your tokenizer for pre-training and domain adaptation

Gautier Dagan, Gabriel Synnaeve, Baptiste Roziere

Differentially Private Decentralized Learning with Random Walks

Edwige Cyffers, Aurélien Bellet, Jalaj Upadhyay

Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples

Andrew Craig Cullen, Shijie Liu, Paul Montague, Sarah Monazam Erfani, Benjamin I. P. Rubinstein

ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback

Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Bingxiang He

Ameliorate Spurious Correlations in Dataset Condensation

Justin CUI, Ruochen Wang, Yuanhao Xiong, Cho-jui Hsieh

Asymptotics of feature learning in two-layer networks after one gradient-step

Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue Lu

Harmonizing Generalization and Personalization in Federated Prompt Learning

Tianyu Cui, Hongxia Li, Jingya Wang, Ye Shi

Learning Latent Space Hierarchical EBM Diffusion Models

Jiali Cui, Tian Han

Major-Minor Mean Field Multi-Agent Reinforcement Learning

Kai Cui, Christian Fabian, Anam Tahir, Heinz Koeppl

Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis

Daniel Csillag, Claudio Jose Struchiner, Guilherme Tegoni Goedert

Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers

Katherine Crowson, Stefan Andreas Baumann, Alex Birch, Tanishq Mathew Abraham, Daniel Z Kaplan

Agent Instructs Large Language Models to be General Zero-Shot Reasoners

Nicholas Crispino, Kyle Montgomery, Fankun Zeng, Dawn Song, Chenguang Wang

Conformal Prediction Sets Improve Human Decision Making

Jesse C. Cresswell, Yi Sui, Bhargava Kumar, Noël Vouitsis

Time Series Diffusion in the Frequency Domain

Jonathan Crabbe, Nicolas Huynh, Jan Pawel Stanczuk, Mihaela van der Schaar

Scaling Laws for the Value of Individual Data Points in Machine Learning

Ian Connick Covert, Wenlong Ji, Tatsunori Hashimoto, James Zou

Statistical Inference Under Constrained Selection Bias

Santiago Cortes-Gomez, Mateo Dulce Rubio, Carlos Miguel Patiño, Bryan Wilder

Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H. Holliday, Bob M. Jacobs

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks

Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai

A2Q+: Improving Accumulator-Aware Weight Quantization

Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig, Yaman Umuroglu

Dynamic Correlation Clustering in Sublinear Update Time

Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis

A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering

Vincent Cohen-Addad, Tommaso d’Orsi, Aida Mousavifar

Multi-View Stochastic Block Models

Vincent Cohen-Addad, Tommaso d’Orsi, Silvio Lattanzi, Rajai Nasser

Perturb-and-Project: Differentially Private Similarities and Marginals

Vincent Cohen-Addad, Tommaso d’Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong

Improving Token-Based World Models with Parallel Observation Prediction

Lior Cohen, KAIXIN WANG, Bingyi Kang, Shie Mannor

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

Nathaniel Cohen, Vladimir Kulikov, Matan Kleiner, Inbar Huberman-Spiegelglas, Tomer Michaeli

CogBench: a large language model walks into a psychology lab

Julian Coda-Forno, Marcel Binz, Jane X Wang, Eric Schulz

VITS : Variational Inference Thompson Sampling for contextual bandits

Pierre Clavier, Tom Huix, Alain Oliviero Durmus

Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens

Ross M Clarke, José Miguel Hernández-Lobato

Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting

Andrea Cini, Danilo Mandic, Cesare Alippi

MusicRL: Aligning Music Generation to Human Preferences

Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic

Prompt-tuning Latent Diffusion Models for Inverse Problems

Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, Mauricio Delbracio

Sampling-based Multi-dimensional Recalibration

Youngseog Chung, Ian Char, Jeff Schneider

How Private are DP-SGD Implementations?

Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity

Tianshu Chu, Dachuan Xu, Wei Yao, Jin Zhang

A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Mohammed Nowaz Rabbani Chowdhury, Meng Wang, Kaoutar El Maghraoui, Naigang Wang, Pin-Yu Chen

Learning Linear Block Error Correction Codes

Yoni Choukroun, Lior Wolf

A connection between Tempering and Entropic Mirror Descent

Nicolas Chopin, Francesca Crucinio, Anna Korba

Online bipartite matching with imperfect advice

Davin Choo, Themistoklis Gouleakis, Chun Kai Ling, Arnab Bhattacharyya

PANDA: Expanded Width-Aware Message Passing Beyond Rewiring

Jeongwhan Choi, Sumin Park, Hyowon Wi, Sung-Bae Cho, Noseong Park

PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning

Hyeong Kyu Choi, Yixuan Li

Embodied CoT Distillation From LLM To Off-the-shelf Agents

wonje choi, Woo Kyung Kim, Minjong Yoo, Honguk Woo

BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges

Hoyong Choi, Nohyun Ki, Hye Won Chung

Listwise Reward Estimation for Offline Preference-based Reinforcement Learning

Heewoong Choi, Sangwon Jung, Hongjoon Ahn, Taesup Moon

Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport

Jaemoo Choi, Jaewoong Choi, Myungjoo Kang

Tilt and Average : Geometric Adjustment of the Last Layer for Recalibration

Gyusang Cho, Chan-Hyun Youn

Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization

Hyuna Cho, Jaeyoon Sim, Guorong Wu, Won Hwa Kim

KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation

Minsik Cho, Mohammad Rastegari, Devang Naik

Hard Tasks First: Multi-Task Reinforcement Learning Through Task Scheduling

Myungsik Cho, Jongeui Park, Suyoung Lee, Youngchul Sung

Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters

Brian M Cho, Yaroslav Mukhin, Kyra Gan, Ivana Malenica

Parameterized Physics-informed Neural Networks for Parameterized PDEs

Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee

Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams

Brian M Cho, Kyra Gan, Nathan Kallus

Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts

Zhi-Yi Chin, Chieh Ming Jiang, Ching-Chun Huang, Pin-Yu Chen, Wei-Chen Chiu

Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants

Isabel Chien, Wessel P Bruinsma, Javier Gonzalez, Richard E. Turner

How Flawed Is ECE? An Analysis via Logit Smoothing

Muthu Chidambaram, Holden Lee, Colin Mcswiggen, Semon Rezchikov

Enhancing Trajectory Prediction through Self-Supervised Waypoint Distortion Prediction

Pranav Singh Chib, Pravendra Singh

MS-TIP: Imputation Aware Pedestrian Trajectory Prediction

Pranav Singh Chib, Achintya Nath, Paritosh Kabra, Ishu Gupta, Pravendra Singh

Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference

Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li

Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning

Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun, Chien Feng, Cho-jui Hsieh

Language Models as Science Tutors

Alexis Chevalier, Jiayi Geng, Alexander Wettig, Howard Chen, Sebastian Mizera

Leveraging (Biased) Information: Multi-armed Bandits with Offline Data

Wang Chi Cheung, Lixing Lyu

Creative Text-to-Audio Generation via Synthesizer Programming

Manuel Cherep, Nikhil Singh, Jessica Shand

Kernel Semi-Implicit Variational Inference

Ziheng Cheng, Longlin Yu, Tianyu Xie, Shiyue Zhang, Cheng Zhang

RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences

Jie Cheng, Gang Xiong, Xingyuan Dai, Qinghai Miao, Yisheng Lv

RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation

Zelei Cheng, Xian Wu, Jiahao Yu, Sabrina Yang, Gang Wang

Can AI Assistants Know What They Don’t Know?

Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu, Wenwei Zhang, Zhangyue Yin

Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior

Shuyu Cheng, yibo miao, Yinpeng Dong, Xiao Yang, Xiao-Shan Gao

Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum

Tin Sum Cheng, Aurelien Lucchi, Anastasis Kratsios, David Belius

GaussianPro: 3D Gaussian Splatting with Progressive Propagation

Kai Cheng, Xiaoxiao Long, Kaizhi Yang, Yao Yao, Wei Yin

BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks

Zhiyuan Cheng, Zhaoyi Liu, Tengda Guo, Shiwei Feng, Dongfang Liu

Causal Inference out of Control: Estimating Performativity without Treatment Randomization

Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner

Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations

Ze Cheng, Zhongkai Hao, Xiaoqiang Wang, Jianing Huang, Youjia Wu

Layerwise Change of Knowledge in Neural Networks

Xu Cheng, Lei Cheng, Zhaoran Peng, Yang Xu, Tian Han

Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context

Xiang Cheng, Yuxin Chen, Suvrit Sra

What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks

Xingwu Chen, Difan Zou

Stacking Deep Set Networks and Pooling by Quantiles

Zhuojun Chen, Xinghua Zhu, Dongzhe Su, Justin C. I. Chuang

ODIN: Disentangled Reward Mitigates Hacking in RLHF

Lichang Chen, Chen Zhu, Jiuhai Chen, Davit Soselia, Tianyi Zhou

On the Trajectory Regularity of ODE-based Diffusion Sampling

Defang Chen, Zhenyu Zhou, Can Wang, Chunhua Shen, Siwei Lyu