ICML 2023

1,828 papers

Why Is Public Pretraining Necessary for Private Model Training?

Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke

Graph Reinforcement Learning for Network Control via Bi-Level Optimization

Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues

Cell-Free Latent Go-Explore

Quentin Gallouédec, Emmanuel Dellandrea

Why Random Pruning Is All We Need to Start Sparse

Advait Harshal Gadhikar, Sohom Mukherjee, Rebekka Burkholz

Hierarchies of Reward Machines

Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo

NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations

Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang

Meta-learning Parameterized Skills

Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris

Accelerated Stochastic Optimization Methods under Quasar-convexity

Qiang Fu, Dongchu Xu, Ashia Camage Wilson

Specializing Smaller Language Models towards Multi-Step Reasoning

Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar Khot

Go Beyond Imagination: Maximizing Episodic Reachability with World Models

Yao Fu, Run Peng, Honglak Lee

MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses

Yang Fu, Ishan Misra, Xiaolong Wang

Simple Hardware-Efficient Long Convolutions for Sequence Modeling

Daniel Y Fu, Elliot L Epstein, Eric Nguyen, Armin W Thomas, Michael Zhang

A Coupled Flow Approach to Imitation Learning

Gideon Joseph Freund, Elad Sarafian, Sarit Kraus

Learning Temporally AbstractWorld Models without Online Experimentation

Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset

SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot

Elias Frantar, Dan Alistarh

A Theoretical Analysis of the Learning Dynamics under Class Imbalance

Emanuele Francazi, Marco Baity-Jesi, Aurelien Lucchi

Last Switch Dependent Bandits with Monotone Payoff Functions

Ayoub Foussoul, Vineet Goyal, Orestis Papadigenopoulos, Assaf Zeevi

Can Forward Gradient Match Backpropagation?

Louis Fournier, Stephane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

Disentangled Generative Models for Robust Prediction of System Dynamics

Stathi Fotiadis, Mario Lino Valencia, Shunlong Hu, Stef Garasto, Chris D Cantwell

Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games

Dylan J Foster, Noah Golowich, Sham M. Kakade

Explainable Data-Driven Optimization: From Context to Decision and Back Again

Alexandre Forel, Axel Parmentier, Thibaut Vidal

ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging

Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna Wardlaw, Grant Mair

User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems

Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez

Adapting to game trees in zero-sum imperfect information games

Côme Fiegel, Pierre Menard, Tadashi Kozuno, Rémi Munos, Vianney Perchet

Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation

Hendrik Fichtenberger, Monika Henzinger, Jalaj Upadhyay

Scaling Laws for Multilingual Neural Machine Translation

Patrick Fernandes, Behrooz Ghorbani, Xavier Garcia, Markus Freitag, Orhan Firat

SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems

Aaron M Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner

Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption

Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux

Non-stationary Reinforcement Learning under General Function Approximation

Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang

Improved Algorithms for White-Box Adversarial Streams

Ying Feng, David Woodruff

Fractional Denoising for 3D Molecular Pre-training

Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-ming Ma, Wei-Ying Ma

Improved Online Learning Algorithms for CTR Prediction in Ad Auctions

Zhe Feng, Christopher Liaw, Zixin Zhou

Weighted Sampling without Replacement for Deep Top-k Classification

Dieqiao Feng, Yuanqi Du, Carla P Gomes, Bart Selman

Why Target Networks Stabilise Temporal Difference Methods

Mattie Fellows, Matthew J. A. Smith, Shimon Whiteson

Parallel Neurosymbolic Integration with Concordia

Jonathan Feldstein, Modestas Jurčius, Efthymia Tsamoura

Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies

Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He

Neural FIM for learning Fisher information metrics from point cloud data

Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf

Robust Collaborative Learning with Linear Gradient Overhead

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot

Regression with Label Permutation in Generalized Linear Model

Guanhua Fang, Ping Li

The Fast Johnson-Lindenstrauss Transform Is Even Faster

Ora Nova Fandina, Mikael Møller Høgsgaard, Kasper Green Larsen

General Covariance Data Augmentation for Neural PDE Solvers

Vladimir Fanaskov, Tianchi Yu, Alexander Rudikov, Ivan Oseledets

Smart Initial Basis Selection for Linear Programs

Zhenan Fan, Xinglu Wang, Oleksandr Yakovenko, Abdullah Ali Sivas, Owen Ren

LSDS++ : Dual Sampling for Accelerated k-means++

Chenglin Fan, Ping Li, Xiaoyun Li

Optimizing DDPM Sampling with Shortcut Fine-Tuning

Ying Fan, Kangwook Lee

Free-Form Variational Inference for Gaussian Process State-Space Models

Xuhui Fan, Edwin V. Bonilla, Terence O’Kane, Scott A Sisson

Predicting Rare Events by Shrinking Towards Proportional Odds

Gregory Faletto, Jacob Bien

Learning Rate Schedules in the Presence of Distribution Shift

Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah

Neural Status Registers

Lukas Faber, Roger Wattenhofer

A Connection between One-Step RL and Critic Regularization in Reinforcement Learning

Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov

Continual Learning in Linear Classification on Separable Data

Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh

Stochastic Gradient Descent under Markovian Sampling Schemes

Mathieu Even

Scaling Spherical CNNs

Carlos Esteves, Jean-Jacques Slotine, Ameesh Makadia

Delayed Bandits: When Do Intermediate Observations Help?

Emmanuel Esposito, Saeed Masoudian, Hao Qiu, Dirk van der Hoeven, Nicolò Cesa-bianchi

Regret Minimization and Convergence to Equilibria in General-sum Markov Games

Liad Erez, Tal Lancewicki, Uri Sherman, Tomer Koren, Yishay Mansour

Learning Perturbations to Explain Time Series Predictions

Joseph Enguehard

Motion Question Answering via Modular Motion Programs

Mark Endo, Joy Hsu, Jiaman Li, Jiajun Wu

A new near-linear time algorithm for k-nearest neighbor search using a compressed cover tree

Yury Elkin, Vitaliy Kurlin

Phase Transitions in the Detection of Correlated Databases

Dor Elimelech, Wasim Huleihel

Improving Graph Neural Networks with Learnable Propagation Operators

Moshe Eliasof, Lars Ruthotto, Eran Treister

Graph Positional Encoding via Random Feature Propagation

Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik

Difference of submodular minimization via DC programming

Marwa El Halabi, George Orfanides, Tim Hoheisel

Fairness in Streaming Submodular Maximization over a Matroid Constraint

Marwa El Halabi, Federico Fusco, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski

Hyperparameters in Reinforcement Learning and How To Tune Them

Theresa Eimer, Marius Lindauer, Roberta Raileanu

Performative Recommendation: Diversifying Content via Strategic Incentives

Itay Eilat, Nir Rosenfeld

E$(n)$ Equivariant Message Passing Simplicial Networks

Floor Eijkelboom, Rob Hesselink, Erik J Bekkers

The Computational Complexity of Concise Hypersphere Classification

Eduard Eiben, Robert Ganian, Iyad A. Kanj, Sebastian Ordyniak, Stefan Szeider

Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces

Javier E. Santos, Zachary R. Fox, Nicholas Lubbers, Yen Ting Lin

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

Alexandre Agm Duval, victor schmidt, Alex Hernández-Garcı́a, Santiago Miret, Fragkiskos D. Malliaros

Neural Diffusion Processes

Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson

Multi-Objective Population Based Training

Arkadiy Dushatskiy, Alexander Chebykin, Tanja Alderliesten, Peter Bosman

Generalization Bounds using Data-Dependent Fractal Dimensions

Benjamin Dupuis, George Deligiannidis, Umut Simsekli

Adaptive Whitening in Neural Populations with Gain-modulating Interneurons

Lyndon Duong, David Lipshutz, David Heeger, Dmitri Chklovskii, Eero P Simoncelli

Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows

Owen M Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic

Integrating Prior Knowledge in Contrastive Learning with Kernel

Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset, Edouard Duchesnay, Pietro Gori

Optimal No-Regret Learning for One-Sided Lipschitz Functions

Paul Duetting, Guru Guruganesh, Jon Schneider, Joshua Ruizhi Wang

Fully Dynamic Submodular Maximization over Matroids

Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam

Evaluating Self-Supervised Learning via Risk Decomposition

Yann Dubois, Tatsunori Hashimoto, Percy Liang

Are Equivariant Equilibrium Approximators Beneficial?

Zhijian Duan, Yunxuan Ma, Xiaotie Deng

Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process

Zhibin Duan, Xinyang Liu, Yudi Su, Yishi Xu, Bo Chen

Are Diffusion Models Vulnerable to Membership Inference Attacks?

Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, Kaidi Xu

Fast Excess Risk Rates via Offset Rademacher Complexity

Chenguang Duan, Yuling Jiao, Lican Kang, Xiliang Lu, Jerry Zhijian Yang

A Flexible Diffusion Model

weitao Du, He Zhang, Tao Yang, Yuanqi Du

Guiding Pretraining in Reinforcement Learning with Large Language Models

Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Tianyuan Yuan

Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation

Jin-Hong Du, Pratik Patil, Arun K. Kuchibhotla

Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows

Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, Min Lin

Multi-task Representation Learning for Pure Exploration in Linear Bandits

Yihan Du, Longbo Huang, Wen Sun

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

Yilun Du, Conor Durkan, Robin STRUDEL, Joshua B. Tenenbaum, Sander Dieleman

PaLM-E: An Embodied Multimodal Language Model

Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery

On the Convergence Rate of Gaussianization with Random Rotations

Felix Draxler, Lars Kühmichel, Armand Rousselot, Jens Müller, Christoph Schnoerr

Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains

Matthew Dowling, Yuan Zhao, Il Memming Park

Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains

Vishwaraj Doshi, Jie Hu, Do Young Eun

Meta-Learning the Inductive Bias of Simple Neural Circuits

Will Dorrell, Maria Yuffa, Peter E. Latham

DoCoFL: Downlink Compression for Cross-Device Federated Learning

Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy

Symmetry-Aware Robot Design with Structured Subgroups

Heng Dong, Junyu Zhang, Tonghan Wang, Chongjie Zhang

Does Sparsity Help in Learning Misspecified Linear Bandits?

Jialin Dong, Lin Yang

Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift

Yijun Dong, Yuege Xie, Rachel Ward

PASTA: Pessimistic Assortment Optimization

Juncheng Dong, Weibin Mo, Zhengling Qi, Cong Shi, Ethan X Fang

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation

Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong

Understand and Modularize Generator Optimization in ELECTRA-style Pretraining

Chengyu Dong, Liyuan Liu, Hao Cheng, Jingbo Shang, Jianfeng Gao