ICML 2024

2,610 papers

Slicing Mutual Information Generalization Bounds for Neural Networks

Kimia Nadjahi, Kristjan Greenewald, Rickard Brüel Gabrielsson, Justin Solomon

Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation

Yididiya Y. Nadew, Xuhui Fan, Christopher John Quinn

Don’t be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance

Saeid Naderiparizi, Xiaoxuan Liang, Setareh Cohan, Berend Zwartsenberg, Frank Wood

Learning in Deep Factor Graphs with Gaussian Belief Propagation

Seth Nabarro, Mark van der Wilk, Andrew Davison

LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning

Hyungho Na, Il-chul Moon

Diffusion Rejection Sampling

Byeonghu Na, Yeongmin Kim, Minsang Park, Donghyeok Shin, Wanmo Kang

Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making

Vivek Myers, Chongyi Zheng, Anca Dragan, Sergey Levine, Benjamin Eysenbach

Saliency strikes back: How filtering out high frequencies improves white-box explanations

Sabine Muzellec, Thomas FEL, Victor Boutin, Léo Andéol, Rufin VanRullen

Test-Time Regret Minimization in Meta Reinforcement Learning

Mirco Mutti, Aviv Tamar

GATE: How to Keep Out Intrusive Neighbors

Nimrah Mustafa, Rebekka Burkholz

Best Arm Identification for Stochastic Rising Bandits

Marco Mussi, Alessandro Montenegro, Francesco Trovò, Marcello Restelli, Alberto Maria Metelli

Factored-Reward Bandits with Intermediate Observations

Marco Mussi, Simone Drago, Marcello Restelli, Alberto Maria Metelli

BAGEL: Bootstrapping Agents by Guiding Exploration with Language

Shikhar Murty, Christopher D Manning, Peter Shaw, Mandar Joshi, Kenton Lee

Autoformalizing Euclidean Geometry

Logan Murphy, Kaiyu Yang, Jialiang Sun, Zhaoyu Li, Anima Anandkumar

Learning to Route Among Specialized Experts for Zero-Shot Generalization

Mohammed Muqeeth, Haokun Liu, Yufan Liu, Colin Raffel

Turnstile ℓ_p leverage score sampling with applications

Alexander Munteanu, Simon Omlor

Optimal bounds for ℓ_p sensitivity sampling via ℓ_2 augmentation

Alexander Munteanu, Simon Omlor

Nash Learning from Human Feedback

Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi azar, Mark Rowland

Individual Fairness in Graph Decomposition

Kamesh Munagala, Govind S. Sankar

Position: Optimization in SciML Should Employ the Function Space Geometry

Johannes Müller, Marius Zeinhofer

Aligning Transformers with Weisfeiler-Leman

Luis Müller, Christopher Morris

Truly No-Regret Learning in Constrained MDPs

Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He

Is Kernel Prediction More Powerful than Gating in Convolutional Neural Networks?

Lorenz K Muller

Active Preference Learning for Large Language Models

William Muldrew, Peter Hayes, Mingtian Zhang, David Barber

SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP

Subhojyoti Mukherjee, Josiah P. Hanna, Robert D Nowak

Noga Mudrik, Gal Mishne, Adam Shabti Charles

Controlled Decoding from Language Models

Sidharth Mudgal, Jong Lee, Harish Ganapathy, Yaguang Li, Tao Wang

On the Second-Order Convergence of Biased Policy Gradient Algorithms

Siqiao Mu, Diego Klabjan

RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis

Yao Mu, Junting Chen, Qing-Long Zhang, Shoufa Chen, Qiaojun Yu

Privacy Attacks in Decentralized Learning

Abdellah El Mrini, Edwige Cyffers, Aurélien Bellet

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs

S Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart

Gibbs Sampling of Continuous Potentials on a Quantum Computer

Arsalan Motamedi, Pooya Ronagh

Position: Levels of AGI for Operationalizing Progress on the Path to AGI

Meredith Ringel Morris, Jascha Sohl-dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe

Position: Future Directions in the Theory of Graph Machine Learning

Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, İsmail İlkan Ceylan

Causal Representation Learning Made Identifiable by Grouping of Observational Variables

Hiroshi Morioka, Aapo Hyvärinen

Lookbehind-SAM: k steps back, 1 step forward

Gonçalo Mordido, Pranshu Malviya, Aristide Baratin, Sarath Chandar

A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models

Taehong Moon, Moonseok Choi, Eunggu Yun, Jongmin Yoon, Gayoung Lee

Learning Optimal Deterministic Policies with Stochastic Policy Gradients

Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli, Matteo Papini

A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks

Behrad Moniri, Donghwan Lee, Hamed Hassani, Edgar Dobriban

Slot Abstractors: Toward Scalable Abstract Visual Reasoning

Shanka Subhra Mondal, Jonathan D. Cohen, Taylor Whittington Webb

A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks

Nicholas Monath, Will Sussman Grathwohl, Michael Boratko, Rob Fergus, Andrew McCallum

Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks

Bjørn Leth Møller, Christian Igel, Kristoffer Knutsen Wickstrøm, Jon Sporring, Robert Jenssen

Language Models with Conformal Factuality Guarantees

Christopher Mohri, Tatsunori Hashimoto

OAK: Enriching Document Representations using Auxiliary Knowledge for Extreme Classification

Shikhar Mohan, Deepak Saini, Anshul Mittal, Sayak Ray Chowdhury, Bhawna Paliwal

Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm

Mimoun Mohamed, François Malgouyres, Valentin Emiya, Caroline Chaux

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

Mohamad Amin Mohamadi, Zhiyuan Li, Lei Wu, Danica J. Sutherland

Error Feedback Can Accurately Compress Preconditioners

Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, Dan Alistarh

TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors

Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang

Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs

Slobodan Mitrovic, Theodore Pan

Position: Intent-aligned AI Systems Must Optimize for Agency Preservation

Catalin Mitelut, Benjamin Smith, Peter Vamplew

Provable Interactive Learning with Hindsight Instruction Feedback

Dipendra Misra, Aldo Pacchiano, Robert E. Schapire

From Inverse Optimization to Feasibility to ERM

Saurabh Kumar Mishra, Anant Raj, Sharan Vaswani

Prodigy: An Expeditiously Adaptive Parameter-Free Learner

Konstantin Mishchenko, Aaron Defazio

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi, Ali Ansari, Sepehr Ghobadi

Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models

Yifei Ming, Yixuan Li

Can Implicit Bias Imply Adversarial Robustness?

Hancheng Min, René Vidal

FlowMM: Generating Materials with Riemannian Flow Matching

Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M Wood

CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes

Peter Mikhael, Itamar Chinn, Regina Barzilay

Interplay of ROC and Precision-Recall AUCs: Theoretical Limits and Practical Implications in Binary Classification

Martin Mihelich, François Castagnos, Charles Dognin

Efficient World Models with Context-Aware Tokenization

Vincent Micheli, Eloi Alonso, Francois Fleuret

Rethinking Momentum Knowledge Distillation in Online Continual Learning

Nicolas Michel, Maorong Wang, Ling Xiao, Toshihiko Yamasaki

DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation

Chenfeng Miao, Qingying Zhu, Minchuan Chen, Wei Hu, Zijian Li

Rethinking Independent Cross-Entropy Loss For Graph-Structured Data

Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu, Ying Wang

Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics

Siqi Miao, Zhiyuan Lu, Mia Liu, Javier Duarte, Pan Li

How Deep Do We Need: Accelerating Training and Inference of Neural ODEs via Control Perspective

Keyan Miao, Konstantinos Gatsis

Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation

Thomas Merth, Qichen Fu, Mohammad Rastegari, Mahyar Najibi

The Illusion of State in State-Space Models

William Merrill, Jackson Petty, Ashish Sabharwal

Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models

Pierre Mergny, Justin Ko, Florent Krzakala

Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data

Xuran Meng, Difan Zou, Yuan Cao

Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification

Yiming Meng, Ruikun Zhou, Amartya Mukherjee, Maxwell Fitzsimmons, Christopher Song

OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization

Xiang Meng, Shibal Ibrahim, Kayhan Behdin, Hussein Hazimeh, Natalia Ponomareva

Position: Tensor Networks are a Valuable Asset for Green AI

Eva Memmel, Clara Menzen, Jetze Schuurmans, Frederiek Wesel, Kim Batselier

On Learning Deep O(n)-Equivariant Hyperspheres

Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck, Andreas Robinson, Cuong Le

IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation

Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi

Matthieu Meeus, Igor Shilov, Manuel Faysse, Yves-Alexandre De Montjoye

Roping in Uncertainty: Robustness and Regularization in Markov Games

Jeremy McMahan, Giovanni Artiglio, Qiaomin Xie

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

Daniel McDuff, Tim Korjakow, Scott Cambo, Jesse Josua Benjamin, Jenny Lee

Incremental Topological Ordering and Cycle Detection with Predictions

Samuel McCauley, Benjamin Moseley, Aidin Niaparast, Shikha Singh

Deep Fusion: Efficient Network Training via Pre-trained Initializations

Hanna Mazzawi, Javier Gonzalvo, Michael Wunder, Sammy Jerome, Benoit Dherin

HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang

Joint Composite Latent Space Bayesian Optimization

Natalie Maus, Zhiyuan Jerry Lin, Maximilian Balandat, Eytan Bakshy

Sampling in Unit Time with Kernel Fisher-Rao Flow

Aimee Maurais, Youssef Marzouk

Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning

Michael Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan, Matthew Thomas Jackson

Hieros: Hierarchical Imagination on Structured State Space Sequence World Models

Paul Mattes, Rainer Schlosser, Ralf Herbrich

Amortized Variational Deep Kernel Learning

Alan L. S. Matias, César Lincoln Mattos, João Paulo Pordeus Gomes, Diego Mesquita

Online Variational Sequential Monte Carlo

Alessandro Mastrototaro, Jimmy Olsson

On the Consistency of Kernel Methods with Dependent Observations

Pierre-François Massiani, Sebastian Trimpe, Friedrich Solowjow

Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference

Luca Masserano, Alexander Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki

On the Tractability of SHAP Explanations under Markovian Distributions

Reda Marzouk, Colin de la Higuera

Using AI Uncertainty Quantification to Improve Human Decision-Making

Laura Marusich, Jonathan Bakdash, Yan Zhou, Murat Kantarcioglu

Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point

David Martı́nez-Rubio, Christophe Roux, Sebastian Pokutta

Expand-and-Cluster: Parameter Recovery of Neural Networks

Flavio Martinelli, Berfin Simsek, Wulfram Gerstner, Johanni Brea

A Unified View of FANOVA: A Comprehensive Bayesian Framework for Component Selection and Estimation

Yosra Marnissi, Maxime Leiber

Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling

Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi

Quantum Theory and Application of Contextual Optimal Transport

Nicola Mariella, Albert Akhriev, Francesco Tacchino, Christa Zoufal, Juan Carlos Gonzalez-Espitia

Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows

Sibylle Marcotte, Remi Gribonval, Gabriel Peyré

No-Regret Reinforcement Learning in Smooth MDPs

Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli

Regression with Multi-Expert Deferral

Anqi Mao, Mehryar Mohri, Yutao Zhong

H-Consistency Guarantees for Regression

Anqi Mao, Mehryar Mohri, Yutao Zhong

Towards General Neural Surrogate Solvers with Specialized Neural Accelerators

Chenkai Mao, Robert Lupoiu, Tianxiang Dai, Mingkun Chen, Jonathan Fan