ICML 2024

2,610 papers

Information-Directed Pessimism for Offline Reinforcement Learning

Alec Koppel, Sujay Bhatt, Jiacheng Guo, Joe Eappen, Mengdi Wang

The Computational Complexity of Finding Second-Order Stationary Points

Andreas Kontogiannis, Vasilis Pollatos, Sotiris Kanellopoulos, Panayotis Mertikopoulos, Aris Pagourtzis

ELF: Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis

Jungil Kong, Junmo Lee, Jeongmin Kim, Beomjeong Kim, Jihoon Park

Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning

Xiangzhe Kong, Wenbing Huang, Yang Liu

Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities

Zhifeng Kong, Arushi Goel, rohan badlani, Wei Ping, Rafael Valle

VideoPoet: A Large Language Model for Zero-Shot Video Generation

Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang

Logistic Variational Bayes Revisited

Michael Komodromos, Marina Evangelou, Sarah Lucie Filippi

On Convergence of Incremental Gradient for Non-convex Smooth Functions

Anastasia Koloskova, Nikita Doikov, Sebastian U Stich, Martin Jaggi

AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion

Adeesh Kolluru, John R. Kitchin

Estimating Barycenters of Distributions with Neural Optimal Transport

Alexander Kolesov, Petr Mokrov, Igor Udovichenko, Milena Gazdieva, Gudmund Pammer

Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth

Kevin Kögler, Aleksandr Shevchenko, Hamed Hassani, Marco Mondelli

Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis

Juyeon Ko, Inho Kong, Dogyun Park, Hyunwoo J. Kim

Provably Scalable Black-Box Variational Inference with Structured Variational Families

Joohwan Ko, Kyurae Kim, Woo Chang Kim, Jacob R. Gardner

DistiLLM: Towards Streamlined Distillation for Large Language Models

Jongwoo Ko, Sungnyun Kim, Tianyi Chen, Se-Young Yun

Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes

Hyunouk Ko, Xiaoming Huo

What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks

Ching-Yun Ko, Pin-Yu Chen, Payel Das, Jeet Mohapatra, Luca Daniel

Environment Design for Inverse Reinforcement Learning

Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis

Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design

Leo Klarner, Tim G. J. Rudner, Garrett M Morris, Charlotte Deane, Yee Whye Teh

Conformal Prediction with Learned Features

Shayan Kiyani, George J. Pappas, Hamed Hassani

From Neurons to Neutrons: A Case Study in Interpretability

Ouail Kitouni, Niklas Nolte, Vı́ctor Samuel Pérez-Dı́az, Sokratis Trifinopoulos, Mike Williams

The Merit of River Network Topology for Neural Flood Forecasting

Nikolas Kirschstein, Yixuan Sun

A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback

Kihyun Kim, Jiawei Zhang, Asuman E. Ozdaglar, Pablo Parrilo

Do Topological Characteristics Help in Knowledge Distillation?

Jungeun Kim, Junwon You, Dongjin Lee, Ha Young Kim, Jae-Hun Jung

One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning

Doyoung Kim, Susik Yoon, Dongmin Park, Youngjun Lee, Hwanjun Song

An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network

Taeyoung Kim, Hongseok Yang

Discovering Features with Synergistic Interactions in Multiple Views

Chohee Kim, Mihaela van der Schaar, Changhee Lee

Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape

Juno Kim, Taiji Suzuki

Polynomial-based Self-Attention for Table Representation Learning

Jayoung Kim, Yehjin Shin, Jeongwhan Choi, Hyowon Wi, Noseong Park

USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval

Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Wittawat Jitkrittum, Veeranjaneyulu Sadhanala

Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time

Sungyoon Kim, Mert Pilanci

Privacy-Preserving Embedding via Look-up Table Evaluation with Fully Homomorphic Encryption

Jae-Yun Kim, Saerom Park, Joohee Lee, Jung Hee Cheon

Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning

Dongkwan Kim, Alice Oh

Data-Efficient Molecular Generation with Hierarchical Textual Inversion

Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, Jinwoo Shin

An LLM Compiler for Parallel Function Calling

Sehoon Kim, Suhong Moon, Ryan Tabrizi, Nicholas Lee, Michael W. Mahoney

Risk-Sensitive Policy Optimization via Predictive CVaR Policy Gradient

Ju-Hyun Kim, Seungki Min

Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training

Yechan Kim, Hwijoon Lim, Dongsu Han

EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning

Jongsuk Kim, Hyeongkeun Lee, Kyeongha Rho, Junmo Kim, Joon Son Chung

Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning

Donghu Kim, Hojoon Lee, Kyungmin Lee, Dongyoon Hwang, Jaegul Choo

Attribute Based Interpretable Evaluation Metrics for Generative Models

Dongkyun Kim, Mingi Kwon, Youngjung Uh

Learning to Scale Logits for Temperature-Conditional GFlowNets

Minsu Kim, Joohwan Ko, Taeyoung Yun, Dinghuai Zhang, Ling Pan

Demystifying SGD with Doubly Stochastic Gradients

Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner

Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts

Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee

Clustered Federated Learning via Gradient-based Partitioning

Heasung Kim, Hyeji Kim, Gustavo De Veciana

Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization

Hyeonah Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park

Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency

Hyeongjin Kim, Sangwon Kim, Dasom Ahn, Jong Taek Lee, Byoung Chul Ko

Double-Step Alternating Extragradient with Increasing Timescale Separation for Finding Local Minimax Points: Provable Improvements

Kyuwon Kim, Donghwan Kim

Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization

Nayeong Kim, Juwon Kang, Sungsoo Ahn, Jungseul Ok, Suha Kwak

Learning to Explore for Stochastic Gradient MCMC

SeungHyun Kim, Seohyeon Jung, Seonghyeon Kim, Juho Lee

Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs

Mingyu Kim, Kim Jun-Seong, Se-Young Yun, Jin-Hwa Kim

Hybrid Neural Representations for Spherical Data

Hyomin Kim, Yunhui Jang, Jaeho Lee, Sungsoo Ahn

ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models

Dongha Kim, Jaesung Hwang, Jongjin Lee, Kunwoong Kim, Yongdai Kim

Active Label Correction for Semantic Segmentation with Foundation Models

Hoyoung Kim, SEHYUN HWANG, Suha Kwak, Jungseul Ok

SqueezeLLM: Dense-and-Sparse Quantization

Sehoon Kim, Coleman Richard Charles Hooper, Amir Gholami, Zhen Dong, Xiuyu Li

Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning

Do-Yeon Kim, Dong-Jun Han, Jun Seo, Jaekyun Moon

CARTE: Pretraining and Transfer for Tabular Learning

Myung Jun Kim, Leo Grinsztajn, Gael Varoquaux

LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging

Jinuk Kim, Marwa El Halabi, Mingi Ji, Hyun Oh Song

Gaussian Plane-Wave Neural Operator for Electron Density Estimation

Seongsu Kim, Sungsoo Ahn

Can Machines Learn the True Probabilities?

Jinsook Kim

Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model

Mikail Khona, Maya Okawa, Jan Hula, Rahul Ramesh, Kento Nishi

Off-policy Evaluation Beyond Overlap: Sharp Partial Identification Under Smoothness

Samir Khan, Martin Saveski, Johan Ugander

Debating with More Persuasive LLMs Leads to More Truthful Answers

Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan

Tuning-Free Stochastic Optimization

Ahmed Khaled, Chi Jin

Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks

Shervin Khalafi, Saurabh Sihag, Alejandro Ribeiro

Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach

Vijay Keswani, Anay Mehrotra, L. Elisa Celis

A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles

Phillip Kerger, Marco Molinaro, Hongyi Jiang, Amitabh Basu

Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models

Akhil Kedia, Mohd Abbas Zaidi, Sushil Khyalia, Jungho Jung, Harshith Goka

DUPLEX: Dual GAT for Complex Embedding of Directed Graphs

Zhaoru Ke, Hang Yu, Jianguo Li, Haipeng Zhang

An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks

Zhifa Ke, Zaiwen Wen, Junyu Zhang

Accelerating Convergence in Bayesian Few-Shot Classification

Tianjun Ke, Haoqun Cao, Feng Zhou

Pluvial Flood Emulation with Hydraulics-informed Message Passing

Arnold Kazadi, James Doss-Gollin, Arlei Lopes da Silva

Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance

Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin

First-Order Manifold Data Augmentation for Regression Learning

Ilya Kaufman, Omri Azencot

Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choice

Masahiro Kato, Akihiro Oga, Wataru Komatsubara, Ryo Inokuchi

Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks

Arjun Karuvally, Terrence Sejnowski, Hava T Siegelmann

Position: Embracing Negative Results in Machine Learning

Florian Karl, Malte Kemeter, Gabriel Dax, Paulina Sierak

Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions

Sanjay Kariyappa, Freddy Lecue, Saumitra Mishra, Christopher Pond, Daniele Magazzeni

Challenges and Considerations in the Evaluation of Bayesian Causal Discovery

Amir Mohammad Karimi Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani

Infinite-Horizon Distributionally Robust Regret-Optimal Control

Taylan Kargin, Joudi Hajar, Vikrant Malik, Babak Hassibi

On the Generalization of Equivariant Graph Neural Networks

Rafal Karczewski, Amauri H Souza, Vikas Garg

On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning

Ari Karchmer

Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar

Adaptive Robust Learning using Latent Bernoulli Variables

Aleksandr Karakulev, Dave Zachariah, Prashant Singh

Position: On the Societal Impact of Open Foundation Models

Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami

Neural Tangent Kernels for Axis-Aligned Tree Ensembles

Ryuichi Kanoh, Mahito Sugiyama

Certifiably Byzantine-Robust Federated Conformal Prediction

Mintong Kang, Zhen Lin, Jimeng Sun, Cao Xiao, Bo Li

Think Before You Act: Decision Transformers with Working Memory

Jikun Kang, Romain Laroche, Xingdi Yuan, Adam Trischler, Xue Liu

C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models

Mintong Kang, Nezihe Merve Gürel, Ning Yu, Dawn Song, Bo Li

Learning Decision Trees and Forests with Algorithmic Recourse

Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike

A Geometric Explanation of the Likelihood OOD Detection Paradox

Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul Krishnan

Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks

Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly

Tell, Don’t Show: Language Guidance Eases Transfer Across Domains in Images and Videos

Tarun Kalluri, Bodhisattwa Prasad Majumder, Manmohan Chandraker

Replicable Learning of Large-Margin Halfspaces

Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas, Felix Zhou

Beyond the Calibration Point: Mechanism Comparison in Differential Privacy

Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert

EvTexture: Event-driven Texture Enhancement for Video Super-Resolution

Dachun Kai, Jiayao Lu, Yueyi Zhang, Xiaoyan Sun

Surprisingly Strong Performance Prediction with Neural Graph Features

Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari

PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels

Praneeth Kacham, Vahab Mirrokni, Peilin Zhong

Leveraging VLM-Based Pipelines to Annotate 3D Objects

Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy Mitra

PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency

Yeonsung Jung, Heecheol Yun, Joonhyung Park, Jin-Hwa Kim, Eunho Yang

Unsupervised Episode Generation for Graph Meta-learning

Jihyeong Jung, Sangwoo Seo, Sungwon Kim, Chanyoung Park

Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization

Kwang-Sung Jun, Jungtaek Kim