ICML 2024

2,610 papers

ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance

Liwen Sun, Abhineet Agarwal, Aaron Kornblith, Bin Yu, Chenyan Xiong

Enforcing Constraints in RNA Secondary Structure Predictions: A Post-Processing Framework Based on the Assignment Problem

Geewon Suh, Gyeongjo Hwang, Seokjun Kang, Doojin Baek, Mingeun Kang

Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction

Arjun Subramonian, Levent Sagun, Yizhou Sun

Revealing Vision-Language Integration in the Brain with Multimodal Networks

Vighnesh Subramaniam, Colin Conwell, Christopher Wang, Gabriel Kreiman, Boris Katz

Whispering Experts: Neural Interventions for Toxicity Mitigation in Language Models

Xavier Suau, Pieter Delobelle, Katherine Metcalf, Armand Joulin, Nicholas Apostoloff

Compositional Image Decomposition with Diffusion Models

Jocelin Su, Nan Liu, Yanbo Wang, Joshua B. Tenenbaum, Yilun Du

From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation

Kun Su, Xiulong Liu, Eli Shlizerman

Learning from Streaming Data when Users Choose

Jinyan Su, Sarah Dean

DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving

Foteini Strati, Sara Mcallister, Amar Phanishayee, Jakub Tarnawski, Ana Klimovic

Designing Decision Support Systems using Counterfactual Prediction Sets

Eleni Straitouri, Manuel Gomez Rodriguez

Online Learning in CMDPs: Handling Stochastic and Adversarial Constraints

Francesco Emanuele Stradi, Jacopo Germano, Gianmarco Genalti, Matteo Castiglioni, Alberto Marchesi

Causal Inference from Competing Treatments

Ana-Andreea Stoica, Vivian Yvonne Nastl, Moritz Hardt

RLVF: Learning from Verbal Feedback without Overgeneralization

Moritz Pascal Stephan, Alexander Khazatsky, Eric Mitchell, Annie S Chen, Sheryl Hsu

ReGAL: Refactoring Programs to Discover Generalizable Abstractions

Elias Stengel-Eskin, Archiki Prasad, Mohit Bansal

Private Truly-Everlasting Robust-Prediction

Uri Stemmer

QORA: Zero-Shot Transfer via Interpretable Object-Relational Model Learning

Gabriel Stella, Dmitri Loguinov

Learning to Intervene on Concept Bottlenecks

David Steinmann, Wolfgang Stammer, Felix Friedrich, Kristian Kersting

Towards Compositionality in Concept Learning

Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong

Partial Optimality in the Linear Ordering Problem

David Stein, Bjoern Andres

Dirichlet Flow Matching with Applications to DNA Sequence Design

Hannes Stärk, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger

Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design

Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola

Grokking Group Multiplication with Cosets

Dashiell Stander, Qinan Yu, Honglu Fan, Stella Biderman

Diffusion Models Encode the Intrinsic Dimension of Data Manifolds

Jan Pawel Stanczuk, Georgios Batzolis, Teo Deveney, Carola-Bibiane Schönlieb

The Fundamental Limits of Least-Privilege Learning

Theresa Stadler, Bogdan Kulynych, Michael Gastpar, Nicolas Papernot, Carmela Troncoso

Contextual Feature Selection with Conditional Stochastic Gates

Ram Dyuthi Sristi, Ofir Lindenbaum, Shira Lifshitz, Maria Lavzin, Jackie Schiller

CHEMREASONER: Heuristic Search over a Large Language Model’s Knowledge Space using Quantum-Chemical Feedback

Henry W. Sprueill, Carl Edwards, Khushbu Agarwal, Mariefel V Olarte, Udishnu Sanyal

Offline Actor-Critic Reinforcement Learning Scales to Large Models

Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch

Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy

Lucas Spangher, Allen M. Wang, Andrew Maris, Myles Stapelberg, Viraj Mehta

Position: A Roadmap to Pluralistic Alignment

Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell L Gordon, Niloofar Mireshghallah

Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates

Zhenqiao Song, Yunlong Zhao, Wenxian Shi, Wengong Jin, Yang Yang

Latent Logic Tree Extraction for Event Sequence Explanation from LLMs

Zitao Song, Chao Yang, Chaojie Wang, Bo An, Shuang Li

Rich-Observation Reinforcement Learning with Continuous Latent Dynamics

Yuda Song, Lili Wu, Dylan J Foster, Akshay Krishnamurthy

Position: Leverage Foundational Models for Black-Box Optimization

Xingyou Song, Yingtao Tian, Robert Tjarko Lange, Chansoo Lee, Yujin Tang

Unveiling the Dynamics of Information Interplay in Supervised Learning

Kun Song, Zhiquan Tan, Bochao Zou, Huimin Ma, Weiran Huang

SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

Jiwon Song, Kyungseok Oh, Taesu Kim, Hyungjun Kim, Yulhwa Kim

Sparse is Enough in Fine-tuning Pre-trained Large Language Models

Weixi Song, Zuchao Li, Lefei Zhang, Hai Zhao, Bo Du

OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos

Ziyang Song, Jinxi Li, Bo Yang

SurfPro: Functional Protein Design Based on Continuous Surface

Zhenqiao Song, Tinglin Huang, Lei Li, Wengong Jin

Multimodal Prototyping for cancer survival prediction

Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag Jayant Vaidya, Alexander Baras

Hybrid Reinforcement Learning from Offline Observation Alone

Yuda Song, Drew Bagnell, Aarti Singh

Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?

Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl

SPADE: Sparsity-Guided Debugging for Deep Neural Networks

Arshia Soltani Moakhar, Eugenia Iofinova, Elias Frantar, Dan Alistarh

On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization

Motahareh Sohrabi, Juan Ramirez, Tianyue H. Zhang, Simon Lacoste-Julien, Jose Gallego-Posada

Probabilistic Modeling of Interpersonal Coordination Processes

Paulo Soares, Adarsh Pyarelal, Meghavarshini Krishnaswamy, Emily Butler, Kobus Barnard

Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs

Andries Petrus Smit, Nathan Grinsztajn, Paul Duckworth, Thomas D Barrett, Arnu Pretorius

Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints

Oscar Smee, Fred Roosta

Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing

Amutheezan Sivagnanam, Ava Pettet, Hunter Lee, Ayan Mukhopadhyay, Abhishek Dubey

Subhomogeneous Deep Equilibrium Models

Pietro Sittoni, Francesco Tudisco

In-Context Reinforcement Learning for Variable Action Spaces

Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Sergey Kolesnikov

SAPG: Split and Aggregate Policy Gradients

Jayesh Singla, Ananye Agarwal, Deepak Pathak

What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions

Raghav Singhal, Mark Goldstein, Rajesh Ranganath

Parallelized Spatiotemporal Slot Binding for Videos

Gautam Singh, Yue Wang, Jiawei Yang, Boris Ivanovic, Sungjin Ahn

Byzantine Resilient and Fast Federated Few-Shot Learning

Ankit Pratap Singh, Namrata Vaswani

PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling

Utsav Singh, Wesley A Suttle, Brian M. Sadler, Vinay P. Namboodiri, Amrit Bedi

Representation Surgery: Theory and Practice of Affine Steering

Shashwat Singh, Shauli Ravfogel, Jonathan Herzig, Roee Aharoni, Ryan Cotterell

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Aaditya K Singh, Ted Moskovitz, Felix Hill, Stephanie C.Y. Chan, Andrew M Saxe

Finite Time Logarithmic Regret Bounds for Self-Tuning Regulation

Rahul Singh, Akshay Mete, Avik Kar, Panganamala Kumar

Domain Generalisation via Imprecise Learning

Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet

Latent variable model for high-dimensional point process with structured missingness

Maksim Sinelnikov, Manuel Haussmann, Harri Lahdesmaki

Position: AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research

Riley Simmons-Edler, Ryan Paul Badman, Shayne Longpre, Kanaka Rajan

Deletion-Anticipative Data Selection with a Limited Budget

Rachael Hwee Ling Sim, Jue Fan, Xiao Tian, Patrick Jaillet, Bryan Kian Hsiang Low

Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features

Thalles Silva, Helio Pedrini, Adı́n Ramı́rez Rivera

On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation

Álvaro Labarca Silva, Denis Parra, Rodrigo Toro Icarte

Embarrassingly Parallel GFlowNets

Tiago Silva, Luiz Max Carvalho, Amauri H Souza, Samuel Kaski, Diego Mesquita

InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation

Jacob Yoke Hong Si, Wendy Yusi Cheng, Michael Cooper, Rahul Krishnan

IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics

Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy S. Vatolin

Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation

Sergei Shumilin, Alexander Ryabov, Nikolay Yavich, Evgeny Burnaev, Vladimir Vanovskiy

Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation

Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb

Towards a Better Theoretical Understanding of Independent Subnetwork Training

Egor Shulgin, Peter Richtarik

TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression

Megh Shukla, Mathieu Salzmann, Alexandre Alahi

WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer

Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish, Prathosh AP, Vaibhav Rajan

Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing

Youwei Shu, Xi Xiao, Derui Wang, Yuxin Cao, Siji Chen

Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis

shirin shoushtari, Jiaming Liu, Edward P. Chandler, M. Salman Asif, Ulugbek S. Kamilov

Allocation Requires Prediction Only if Inequality Is Low

Ali Shirali, Rediet Abebe, Moritz Hardt

Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer

Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu, Yuxuan Li

Statistical Test for Attention Maps in Vision Transformers

Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka, Vo Nguyen Le Duy, Kouichi Taji

Causal Discovery with Fewer Conditional Independence Tests

Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler

Neural-Kernel Conditional Mean Embeddings

Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic

Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts

Jiang-Xin Shi, Tong Wei, Zhi Zhou, Jie-Jing Shao, Xin-Yan Han

Why Larger Language Models Do In-context Learning Differently?

Zhenmei Shi, Junyi Wei, Zhuoyan Xu, Yingyu Liang

CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers

Dachuan Shi, Chaofan Tao, Anyi Rao, Zhendong Yang, Chun Yuan

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty

Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman

LCA-on-the-Line: Benchmarking Out of Distribution Generalization with Class Taxonomies

Jia Shi, Gautam Rajendrakumar Gare, Jinjin Tian, Siqi Chai, Zhiqiu Lin

OT-CLIP: Understanding and Generalizing CLIP via Optimal Transport

Liangliang Shi, Jack Fan, Junchi Yan

Double Momentum Method for Lower-Level Constrained Bilevel Optimization

Wanli Shi, Yi Chang, Bin Gu

Rate-Optimal Policy Optimization for Linear Markov Decision Processes

Uri Sherman, Alon Cohen, Tomer Koren, Yishay Mansour

ReLUs Are Sufficient for Learning Implicit Neural Representations

Joseph Shenouda, Yamin Zhou, Robert D Nowak

Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

Han Shen, Zhuoran Yang, Tianyi Chen

Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains

Junhong Shen, Neil Tenenholtz, James Brian Hall, David Alvarez-Melis, Nicolo Fusi

Adaptive Stabilization Based on Machine Learning for Column Generation

Yunzhuang Shen, Yuan Sun, Xiaodong Li, Zhiguang Cao, Andrew Eberhard

Position: Do pretrained Transformers Learn In-Context by Gradient Descent?

Lingfeng Shen, Aayush Mishra, Daniel Khashabi

Thermometer: Towards Universal Calibration for Large Language Models

Maohao Shen, Subhro Das, Kristjan Greenewald, Prasanna Sattigeri, Gregory W. Wornell

Variational Learning is Effective for Large Deep Networks

Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi

Exploring the Complexity of Deep Neural Networks through Functional Equivalence

Guohao Shen

Reducing sequential change detection to sequential estimation

Shubhanshu Shekhar, Aaditya Ramdas

Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models

Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali Thabet

Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models

Louis Sharrock, Jack Simons, Song Liu, Mark Beaumont

Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation

Kartik Sharma, Srijan Kumar, Rakshit Trivedi

How Far Can Fairness Constraints Help Recover From Biased Data?

Mohit Sharma, Amit Deshpande

Learning Decision Policies with Instrumental Variables through Double Machine Learning

Daqian Shao, Ashkan Soleymani, Francesco Quinzan, Marta Kwiatkowska