ICML 2024

2,610 papers

Linguistic Calibration of Long-Form Generations

Neil Band, Xuechen Li, Tengyu Ma, Tatsunori Hashimoto

Fair Resource Allocation in Multi-Task Learning

Hao Ban, Kaiyi Ji

Parsimonious Learning-Augmented Approximations for Dense Instances of NP-hard Problems

Evripidis Bampis, Bruno Escoffier, Michalis Xefteris

Analyzing D^α seeding for k-means

Etienne Bamas, Sai Ganesh Nagarajan, Ola Svensson

On the Identifiability of Switching Dynamical Systems

Carles Balsells-Rodas, Yixin Wang, Yingzhen Li

A Field Guide for Pacing Budget and ROS Constraints

Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni

Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better

Vicente Balmaseda, Ying Xu, Yixin Cao, Nate Veldt

How Learning by Reconstruction Produces Uninformative Features For Perception

Randall Balestriero, Yann LeCun

Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation

Randall Balestriero, Romain Cosentino, Sarath Shekkizhar

Memory Consolidation Enables Long-Context Video Understanding

Ivana Balazevic, Yuge Shi, Pinelopi Papalampidi, Rahma Chaabouni, Skanda Koppula

Adversarial Attacks on Combinatorial Multi-Armed Bandits

Rishab Balasubramanian, Jiawei Li, Prasad Tadepalli, Huazheng Wang, Qingyun Wu

Disentanglement Learning via Topology

Nikita Balabin, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov

An Explicit Frame Construction for Normalizing 3D Point Clouds

Justin Baker, Shih-Hsin Wang, Tommaso De Fernex, Bao Wang

Image Hijacks: Adversarial Images can Control Generative Models at Runtime

Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons

Constrained Ensemble Exploration for Unsupervised Skill Discovery

Chenjia Bai, Rushuai Yang, Qiaosheng Zhang, Kang Xu, Yi Chen

On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition

Yunyan Bai, Yuxing Liu, Luo Luo

Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance

Mingyuan Bai, Wei Huang, Tenghui Li, Andong Wang, Junbin Gao

QBMK: Quantum-based Matching Kernels for Un-attributed Graphs

Lu Bai, Lixin Cui, Ming Li, Yue Wang, Edwin Hancock

Simulation of Graph Algorithms with Looped Transformers

Artur Back de Luca, Kimon Fountoulakis

The Pitfalls of Next-Token Prediction

Gregor Bachmann, Vaishnavh Nagarajan

Differentiable Weightless Neural Networks

Alan Tendler Leibel Bacellar, Zachary Susskind, Mauricio Breternitz Jr, Eugene John, Lizy Kurian John

Online Matrix Completion: A Collaborative Approach with Hott Items

Dheeraj Baby, Soumyabrata Pal

HyperFields: Towards Zero-Shot Generation of NeRFs from Text

Sudarshan Babu, Richard Liu, Avery Zhou, Michael Maire, Greg Shakhnarovich

What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis

Waı̈ss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos

Bipartite Matching in Massive Graphs: A Tight Analysis of EDCS

Amir Azarmehr, Soheil Behnezhad, Mohammad Roghani

Switching the Loss Reduces the Cost in Batch Reinforcement Learning

Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney

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

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond

Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni

Agnostic Sample Compression Schemes for Regression

Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi

Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing

Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy

How Free is Parameter-Free Stochastic Optimization?

Amit Attia, Tomer Koren

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

Hugo Attali, Davide Buscaldi, Nathalie Pernelle

Bifurcated Attention for Single-Context Large-Batch Sampling

Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Haifeng Qian, Hantian Ding

Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages

Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy Nguyen, Kunal Talwar

Translation Equivariant Transformer Neural Processes

Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou

Learning the Target Network in Function Space

Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor

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

Jonas Arruda, Yannik Schälte, Clemens Peiter, Olga Teplytska, Ulrich Jaehde

Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing

Gabriel Arpino, Xiaoqi Liu, Ramji Venkataramanan

Simple linear attention language models balance the recall-throughput tradeoff

Simran Arora, Sabri Eyuboglu, Michael Zhang, Aman Timalsina, Silas Alberti

Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs

Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce

Causal Action Influence Aware Counterfactual Data Augmentation

Núria Armengol Urpı́, Marco Bagatella, Marin Vlastelica, Georg Martius

Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving

Sohei Arisaka, Qianxiao Li

Unsupervised Concept Discovery Mitigates Spurious Correlations

Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish

Practical Performance Guarantees for Pipelined DNN Inference

Aaron Archer, Matthew Fahrbach, Kuikui Liu, Prakash Prabhu

A Rate-Distortion View of Uncertainty Quantification

Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen, Artur Dubrawski

Online conformal prediction with decaying step sizes

Anastasios Nikolas Angelopoulos, Rina Barber, Stephen Bates

Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching

Rico Angell, Andrew McCallum

Adaptive Observation Cost Control for Variational Quantum Eigensolvers

Christopher J. Anders, Kim Andrea Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli

Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing

Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz

Navigating Scaling Laws: Compute Optimality in Adaptive Model Training

Sotiris Anagnostidis, Gregor Bachmann, Imanol Schlag, Thomas Hofmann

Training-Free Long-Context Scaling of Large Language Models

Chenxin An, Fei Huang, Jun Zhang, Shansan Gong, Xipeng Qiu

WAVES: Benchmarking the Robustness of Image Watermarks

Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu

Scalable Online Exploration via Coverability

Philip Amortila, Dylan J Foster, Akshay Krishnamurthy

Generalization Error of Graph Neural Networks in the Mean-field Regime

Gholamali Aminian, Yixuan He, Gesine Reinert, Lukasz Szpruch, Samuel N. Cohen

Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency

Alan Nawzad Amin, Andrew Gordon Wilson

Fast Algorithms for Hypergraph PageRank with Applications to Semi-Supervised Learning

Konstantinos Ameranis, Adela Frances DePavia, Lorenzo Orecchia, Erasmo Tani

Robust Graph Matching when Nodes are Corrupt

Taha Ameen, Bruce Hajek

Stationarity without mean reversion in improper Gaussian processes

Luca Ambrogioni

Hyperbolic Optimizer as a Dynamical System

Nico Alvarado, Hans Lobel

Position: Stop Making Unscientific AGI Performance Claims

Patrick Altmeyer, Andrew M. Demetriou, Antony Bartlett, Cynthia C. S. Liem

Beyond the Norms: Detecting Prediction Errors in Regression Models

Andres Altieri, Marco Romanelli, Georg Pichler, Florence Alberge, Pablo Piantanida

Robust and Conjugate Gaussian Process Regression

Matias Altamirano, François-Xavier Briol, Jeremias Knoblauch

Predicting Dose-Response Curves with Deep Neural Networks

Pedro Alonso Campana, Paul Prasse, Tobias Scheffer

The Privacy Power of Correlated Noise in Decentralized Learning

Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui

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

Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot

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

Zeyuan Allen-Zhu, Yuanzhi Li

Unsupervised Evaluation of Code LLMs with Round-Trip Correctness

Miltiadis Allamanis, Sheena Panthaplackel, Pengcheng Yin

No Dimensional Sampling Coresets for Classification

Meysam Alishahi, Jeff M. Phillips

On the Weight Dynamics of Deep Normalized Networks

Christian H.X. Ali Mehmeti-Göpel, Michael Wand

Evaluation of Test-Time Adaptation Under Computational Time Constraints

Motasem Alfarra, Hani Itani, Alejandro Pardo, Shyma Yaser Alhuwaider, Merey Ramazanova

Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic

Nicolas Alder, Ralf Herbrich

Evaluating Model Bias Requires Characterizing its Mistakes

Isabela Albuquerque, Jessica Schrouff, David Warde-Farley, Ali Taylan Cemgil, Sven Gowal

Stochastic Interpolants with Data-Dependent Couplings

Michael Samuel Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden

Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making

Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager, Sheila A. McIlraith

Gaussian Processes on Cellular Complexes

Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth

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

Ahmet Alacaoglu, Donghwan Kim, Stephen Wright

Nonlinear Filtering with Brenier Optimal Transport Maps

Mohammad Al-Jarrah, Niyizhen Jin, Bamdad Hosseini, Amirhossein Taghvaei

In-Context Language Learning: Architectures and Algorithms

Ekin Akyürek, bailin wang, Yoon Kim, Jacob Andreas

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Tara Akhound-Sadegh, Jarrid Rector-Brooks, Joey Bose, Sarthak Mittal, Pablo Lemos

Encodings for Prediction-based Neural Architecture Search

Yash Akhauri, Mohamed S Abdelfattah

Learning Mixtures of Gaussian Processes through Random Projection

Emmanuel Akeweje, Mimi Zhang

Improving Computational Complexity in Statistical Models with Local Curvature Information

Pedram Akbarian, Tongzheng Ren, JIACHENG ZHUO, Sujay Sanghavi, nhật Hồ

Triple Changes Estimator for Targeted Policies

Sina Akbari, Negar Kiyavash

Not all distributional shifts are equal: Fine-grained robust conformal inference

Jiahao Ai, Zhimei Ren

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

Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook, Yan Dai

How to Escape Sharp Minima with Random Perturbations

Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra

OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models

Ali Ahmaditeshnizi, Wenzhi Gao, Madeleine Udell

Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs

Sara Ahmadian, Edith Cohen

Distinguishing the Knowable from the Unknowable with Language Models

Gustaf Ahdritz, Tian Qin, Nikhil Vyas, Boaz Barak, Benjamin L. Edelman

Policy Evaluation for Variance in Average Reward Reinforcement Learning

Shubhada Agrawal, Prashanth L A, Siva Theja Maguluri

LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions

Victor Agostinelli III, Sanghyun Hong, Lizhong Chen

CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks

Shashank Agnihotri, Steffen Jung, Margret Keuper

ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints

Akhil Agnihotri, Rahul Jain, Haipeng Luo

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

Alekh Agarwal, Jian Qian, Alexander Rakhlin, Tong Zhang

Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements

Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta

Probabilistic Generating Circuits - Demystified

Sanyam Agarwal, Markus Bläser

Learning to Play Atari in a World of Tokens

Pranav Agarwal, Sheldon Andrews, Samira Ebrahimi Kahou

CHAI: Clustered Head Attention for Efficient LLM Inference

Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee

REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates

Arshia Afzal, Grigorios Chrysos, Volkan Cevher, Mahsa Shoaran

Optimal Coresets for Low-Dimensional Geometric Median

Peyman Afshani, Chris Schwiegelshohn