ICML 2024

2,610 papers

Language Generation with Strictly Proper Scoring Rules

Chenze Shao, Fandong Meng, Yijin Liu, Jie Zhou

On Multi-Armed Bandit with Impatient Arms

Yuming Shao, Zhixuan Fang

Improved Dimensionality Dependence for Zeroth-Order Optimisation over Cross-Polytopes

Weijia Shao

On Online Experimentation without Device Identifiers

Shiv Shankar, Ritwik Sinha, Madalina Fiterau

Improved Generalization of Weight Space Networks via Augmentations

Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya

DNCs Require More Planning Steps

Yara Shamshoum, Nitzan Hodos, Yuval Sieradzki, Assaf Schuster

The Balanced-Pairwise-Affinities Feature Transform

Daniel Shalam, Simon Korman

Evaluation of Trajectory Distribution Predictions with Energy Score

Novin Shahroudi, Mihkel Lepson, Meelis Kull

A Multimodal Automated Interpretability Agent

Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang, Achyuta Rajaram, Evan Hernandez

Decomposing and Editing Predictions by Modeling Model Computation

Harshay Shah, Andrew Ilyas, Aleksander Madry

Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models

Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine

Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity

Hyunki Seong, Hyunchul Shim

Retrieval-Augmented Score Distillation for Text-to-3D Generation

Junyoung Seo, Susung Hong, Wooseok Jang, Inès Hyeonsu Kim, Min-Seop Kwak

Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models

Bilgehan Sel, Ahmad Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin

Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!

Milad Sefidgaran, Romain Chor, Abdellatif Zaidi, Yijun Wan

Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes

Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar

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

Paul Steven Scotti, Mihir Tripathy, Cesar Torrico, Reese Kneeland, Tong Chen

Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials

Jonathan Scott, Áine Cahill

Differentiability and Optimization of Multiparameter Persistent Homology

Luis Scoccola, Siddharth Setlur, David Loiseaux, Mathieu Carrière, Steve OUDOT

Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments

Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel

Towards Scalable and Versatile Weight Space Learning

Konstantin Schürholt, Michael W. Mahoney, Damian Borth

In-Context Learning Agents Are Asymmetric Belief Updaters

Johannes A. Schubert, Akshay Kumar Jagadish, Marcel Binz, Eric Schulz

Simultaneous identification of models and parameters of scientific simulators

Cornelius Schröder, Jakob H. Macke

Asymptotics of Learning with Deep Structured (Random) Features

Dominik Schröder, Daniil Dmitriev, Hugo Cui, Bruno Loureiro

Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization

Liam Schramm, Abdeslam Boularias

Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations

Henrik Schopmans, Pascal Friederich

Online Learning with Bounded Recall

Jon Schneider, Kiran Vodrahalli

Implicit Representations for Constrained Image Segmentation

Jan Philipp Schneider, Mishal Fatima, Jovita Lukasik, Andreas Kolb, Margret Keuper

Regularized Q-learning through Robust Averaging

Peter Schmitt-Förster, Tobias Sutter

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference

Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Koethe, Paul-Christian Bürkner

Tilt your Head: Activating the Hidden Spatial-Invariance of Classifiers

Johann Schmidt, Sebastian Stober

Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Christian Schlarmann, Naman Deep Singh, Francesco Croce, Matthias Hein

DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems

Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov

Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling

Yair Schiff, Chia Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu

Incentivized Learning in Principal-Agent Bandit Games

Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines

Parallel Affine Transformation Tuning of Markov Chain Monte Carlo

Philip Schär, Michael Habeck, Daniel Rudolf

MoMo: Momentum Models for Adaptive Learning Rates

Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower

A Fixed-Point Approach for Causal Generative Modeling

meyer scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma

A fast algorithm to simulate nonlinear resistive networks

Benjamin Scellier

Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?

M. Saquib Sarfraz, Mei-Yen Chen, Lukas Layer, Kunyu Peng, Marios Koulakis

Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

Nikhil Sardana, Jacob Portes, Sasha Doubov, Jonathan Frankle

A sampling theory perspective on activations for implicit neural representations

Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko, Alexander Long, Simon Lucey

EvIL: Evolution Strategies for Generalisable Imitation Learning

Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster

Meta Evidential Transformer for Few-Shot Open-Set Recognition

Hitesh Sapkota, Krishna Prasad Neupane, Qi Yu

Sparse and Structured Hopfield Networks

Saul José Rodrigues Dos Santos, Vlad Niculae, Daniel C Mcnamee, Andre Martins

A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization

Sebastian Sanokowski, Sepp Hochreiter, Sebastian Lehner

Promoting External and Internal Equities Under Ex-Ante/Ex-Post Metrics in Online Resource Allocation

Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu

Transformers, parallel computation, and logarithmic depth

Clayton Sanford, Daniel Hsu, Matus Telgarsky

Differentially Private Representation Learning via Image Captioning

Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma

How do Transformers Perform In-Context Autoregressive Learning ?

Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré

Stay on Topic with Classifier-Free Guidance

Guillaume Sanchez, Alexander Spangher, Honglu Fan, Elad Levi, Stella Biderman

Proactive Detection of Voice Cloning with Localized Watermarking

Robin San Roman, Pierre Fernandez, Hady Elsahar, Alexandre Defossez, Teddy Furon

Predictive Coding beyond Correlations

Tommaso Salvatori, Luca Pinchetti, Amine M’Charrak, Beren Millidge, Thomas Lukasiewicz

Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency

Sudeep Salgia, Sattar Vakili, Qing Zhao

Learning from Integral Losses in Physics Informed Neural Networks

Ehsan Saleh, Saba Ghaffari, Tim Bretl, Luke Olson, Matthew West

Second-Order Uncertainty Quantification: A Distance-Based Approach

Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier

An Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization

Emre Sahinoglu, Shahin Shahrampour

I/O Complexity of Attention, or How Optimal is FlashAttention?

Barna Saha, Christopher Ye

Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models

Som Sagar, Aditya Taparia, Ransalu Senanayake

Fast Adversarial Attacks on Language Models In One GPU Minute

Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan, Priyatham Kattakinda, Atoosa Chegini

Align Your Steps: Optimizing Sampling Schedules in Diffusion Models

Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis

Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT

Jon Saad-Falcon, Daniel Y Fu, Simran Arora, Neel Guha, Christopher Re

Tandem Transformers for Inference Efficient LLMs

Aishwarya P S, Pranav Ajit Nair, Yashas Samaga B L, Toby James Boyd, Sanjiv Kumar

Operator SVD with Neural Networks via Nested Low-Rank Approximation

Jongha Jon Ryu, Xiangxiang Xu, Hasan Sabri Melihcan Erol, Yuheng Bu, Lizhong Zheng

Gambling-Based Confidence Sequences for Bounded Random Vectors

Jongha Jon Ryu, Gregory W. Wornell

Explorations of Self-Repair in Language Models

Cody Rushing, Neel Nanda

Rolling Diffusion Models

David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom

Generalizing Orthogonalization for Models with Non-Linearities

David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler

Position: Amazing Things Come From Having Many Good Models

Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr

Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving

Alexander Rudikov, Vladimir Fanaskov, Ekaterina Muravleva, Yuri M. Laevsky, Ivan Oseledets

Modelling Microbial Communities with Graph Neural Networks

Albane Ruaud, Cansu Sancaktar, Marco Bagatella, Christoph Ratzke, Georg Martius

One-Shot Strategic Classification Under Unknown Costs

Elan Rosenfeld, Nir Rosenfeld

Position: Application-Driven Innovation in Machine Learning

David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti

Position: Mission Critical – Satellite Data is a Distinct Modality in Machine Learning

Esther Rolf, Konstantin Klemmer, Caleb Robinson, Hannah Kerner

LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views

Yuji Roh, Qingyun Liu, Huan Gui, Zhe Yuan, Yujin Tang

Position: Key Claims in LLM Research Have a Long Tail of Footnotes

Anna Rogers, Sasha Luccioni

Universal Gradient Methods for Stochastic Convex Optimization

Anton Rodomanov, Ali Kavis, Yongtao Wu, Kimon Antonakopoulos, Volkan Cevher

Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks

Zachary Robertson, Sanmi Koyejo

Early Time Classification with Accumulated Accuracy Gap Control

Liran Ringel, Regev Cohen, Daniel Freedman, Michael Elad, Yaniv Romano

SparQ Attention: Bandwidth-Efficient LLM Inference

Luka Ribar, Ivan Chelombiev, Luke Hudlass-Galley, Charlie Blake, Carlo Luschi

Position: Technical Research and Talent is Needed for Effective AI Governance

Anka Reuel, Lisa Soder, Benjamin Bucknall, Trond Arne Undheim

Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems

Roie Reshef, Kfir Yehuda Levy

Plug-and-Play image restoration with Stochastic deNOising REgularization

Marien Renaud, Jean Prost, Arthur Leclaire, Nicolas Papadakis

CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model

Milong Ren, Tian Zhu, Haicang Zhang

Rejuvenating image-GPT as Strong Visual Representation Learners

Sucheng Ren, Zeyu Wang, Hongru Zhu, Junfei Xiao, Alan Yuille

Hybrid Inverse Reinforcement Learning

Juntao Ren, Gokul Swamy, Steven Wu, Drew Bagnell, Sanjiban Choudhury

TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules

Weijieying Ren, Xiaoting Li, Huiyuan Chen, Vineeth Rakesh, Zhuoyi Wang

Optimal Batched Linear Bandits

Xuanfei Ren, Tianyuan Jin, Pan Xu

Position: Understanding LLMs Requires More Than Statistical Generalization

Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel

Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization

Sam Reifenstein, Timothee Leleu, Yoshihisa Yamamoto

Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States

Noam Razin, Yotam Alexander, Edo Cohen-Karlik, Raja Giryes, Amir Globerson

Provably Robust DPO: Aligning Language Models with Noisy Feedback

Sayak Ray Chowdhury, Anush Kini, Nagarajan Natarajan

Fair Federated Learning via the Proportional Veto Core

Bhaskar Ray Chaudhury, Aniket Murhekar, Zhuowen Yuan, Bo Li, Ruta Mehta

Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion

Ishaan Singh Rawal, Alexander Matyasko, Shantanu Jaiswal, Basura Fernando, Cheston Tan

Unveiling Privacy, Memorization, and Input Curvature Links

Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi, Kaushik Roy

Challenges in Training PINNs: A Loss Landscape Perspective

Pratik Rathore, Weimu Lei, Zachary Frangella, Lu Lu, Madeleine Udell

Generalization to New Sequential Decision Making Tasks with In-Context Learning

Sharath Chandra Raparthy, Eric Hambro, Robert Kirk, Mikael Henaff, Roberta Raileanu

Position: The Reasonable Person Standard for AI

Sunayana Rane

Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning

Aditya Ramesh, Kenny John Young, Louis Kirsch, Jürgen Schmidhuber

Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks

Rahul Ramesh, Ekdeep Singh Lubana, Mikail Khona, Robert P. Dick, Hidenori Tanaka