ICML 2023

1,828 papers

Robust Camera Pose Refinement for Multi-Resolution Hash Encoding

Hwan Heo, Taekyung Kim, Jiyoung Lee, Jaewon Lee, Soohyun Kim

A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

Mikael Henaff, Minqi Jiang, Roberta Raileanu

Training-Free Neural Active Learning with Initialization-Robustness Guarantees

Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low

Group Equivariant Fourier Neural Operators for Partial Differential Equations

Jacob Helwig, Xuan Zhang, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch

Distance Weighted Supervised Learning for Offline Interaction Data

Joey Hejna, Jensen Gao, Dorsa Sadigh

Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification

Florian Heinrichs, Mavin Heim, Corinna Weber

Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting

Jonathan Hehir, Daniel Ting, Graham Cormode

CRISP: Curriculum based Sequential neural decoders for Polar code family

S Ashwin Hebbar, Viraj Vivek Nadkarni, Ashok Vardhan Makkuva, Suma Bhat, Sewoong Oh

Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes

Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu

Contrastive Learning Meets Homophily: Two Birds with One Stone

Dongxiao He, Jitao Zhao, Rui Guo, Zhiyong Feng, Di Jin

Domain Adaptation for Time Series Under Feature and Label Shifts

Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis

A Generalization of ViT/MLP-Mixer to Graphs

Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun

Width and Depth Limits Commute in Residual Networks

Soufiane Hayou, Greg Yang

Sampling-based Nyström Approximation and Kernel Quadrature

Satoshi Hayakawa, Harald Oberhauser, Terry Lyons

Counterfactual Analysis in Dynamic Latent State Models

Martin B Haugh, Raghav Singal

Global Context Vision Transformers

Ali Hatamizadeh, Hongxu Yin, Greg Heinrich, Jan Kautz, Pavlo Molchanov

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting

Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park

Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients

Marc Harkonen, Markus Lange-Hegermann, Bogdan Raita

Algorithmic Collective Action in Machine Learning

Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic

GNOT: A General Neural Operator Transformer for Operator Learning

Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong

Coupled Variational Autoencoder

Xiaoran Hao, Patrick Shafto

Leveraging Demonstrations to Improve Online Learning: Quality Matters

Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen

On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline

Nicklas Hansen, Zhecheng Yuan, Yanjie Ze, Tongzhou Mu, Aravind Rajeswaran

Learning Physical Models that Can Respect Conservation Laws

Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney

Total Variation Graph Neural Networks

Jonas Berg Hansen, Filippo Maria Bianchi

System Identification of Neural Systems: If We Got It Right, Would We Know?

Yena Han, Tomaso A Poggio, Brian Cheung

Alternately Optimized Graph Neural Networks

Haoyu Han, Xiaorui Liu, Haitao Mao, MohamadAli Torkamani, Feng Shi

On the Impact of Knowledge Distillation for Model Interpretability

Hyeongrok Han, Siwon Kim, Hyun-Soo Choi, Sungroh Yoon

Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition

Boran Han

Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees

Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta

Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction

Daniel Haider, Martin Ehler, Peter Balazs

Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning

Seungwoong Ha, Hawoong Jeong

Conformal Prediction Sets for Graph Neural Networks

Soroush H. Zargarbashi, Simone Antonelli, Aleksandar Bojchevski

Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network

Marie Guyomard, Susana Barbosa, Lionel Fillatre

DRew: Dynamically Rewired Message Passing with Delay

Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni

Conditionally Strongly Log-Concave Generative Models

Florentin Guth, Etienne Lempereur, Joan Bruna, Stephane Mallat

Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal

Naresh Kumar Gurulingan, Bahram Zonooz, Elahe Arani

Online Platt Scaling with Calibeating

Chirag Gupta, Aaditya Ramdas

GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets

Shubham Gupta, Sahil Manchanda, Sayan Ranu, Srikanta J. Bedathur

High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors

Shivam Gupta, Jasper C.H. Lee, Eric Price

Identifying Useful Learnwares for Heterogeneous Label Spaces

Lan-Zhe Guo, Zhi Zhou, Yu-feng Li, Zhi-Hua Zhou

Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms

Xingzhuo Guo, Yuchen Zhang, Jianmin Wang, Mingsheng Long

LongCoder: A Long-Range Pre-trained Language Model for Code Completion

Daya Guo, Canwen Xu, Nan Duan, Jian Yin, Julian McAuley

Graph Neural Networks with Learnable and Optimal Polynomial Bases

Yuhe Guo, Zhewei Wei

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

Minghao Guo, Veronika Thost, Samuel W Song, Adithya Balachandran, Payel Das

FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction

Yongxin Guo, Xiaoying Tang, Tao LIN

Linkless Link Prediction via Relational Distillation

Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V Chawla

Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano

Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi

Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP

Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang

FeDXL: Provable Federated Learning for Deep X-Risk Optimization

Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang

Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships

Yaming Guo, Kai Guo, Xiaofeng Cao, Tieru Wu, Yi Chang

Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design

Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael Rabbat

Conformalization of Sparse Generalized Linear Models

Etash Kumar Guha, Eugene Ndiaye, Xiaoming Huo

On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances

Aritra Guha, nhật Hồ, Xuanlong Nguyen

DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design

Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng

NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion

Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M. Susskind, Christian Theobalt

EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression

Kaja Gruntkowska, Alexander Tyurin, Peter Richtarik

Hidden Symmetries of ReLU Networks

Elisenda Grigsby, Kathryn Lindsey, David Rolnick

On Sampling with Approximate Transport Maps

Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrie

Topological Point Cloud Clustering

Vincent Peter Grande, Michael T Schaub

Formalizing Preferences Over Runtime Distributions

Devon R. Graham, Kevin Leyton-brown, Tim Roughgarden

Adaptive Annealed Importance Sampling with Constant Rate Progress

Shirin Goshtasbpour, Victor Cohen, Fernando Pérez-Cruz

Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity

Eduard Gorbunov, Adrien Taylor, Samuel Horváth, Gauthier Gidel

Learning Distributions over Quantum Measurement Outcomes

Weiyuan Gong, Scott Aaronson

Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues

Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stéphan Clémençon

Aligning Language Models with Preferences through f-divergence Minimization

Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu

Efficient RL via Disentangled Environment and Agent Representations

Kevin Gmelin, Shikhar Bahl, Russell Mendonca, Deepak Pathak

Adversarial robustness of amortized Bayesian inference

Manuel Gloeckler, Michael Deistler, Jakob H. Macke

Multicalibration as Boosting for Regression

Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell

Generalized Disparate Impact for Configurable Fairness Solutions in ML

Luca Giuliani, Eleonora Misino, Michele Lombardi

Looped Transformers as Programmable Computers

Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee

Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat

Shantanu Ghosh, Ke Yu, Forough Arabshahi, Kayhan Batmanghelich

Harmonic Neural Networks

Atiyo Ghosh, Antonio Andrea Gentile, Mario Dagrada, Chul Lee, Seong-Hyok Sean Kim

Reinforcement Learning from Passive Data via Latent Intentions

Dibya Ghosh, Chethan Anand Bhateja, Sergey Levine

Contextual Reliability: When Different Features Matter in Different Contexts

Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca Dragan, Aditi Raghunathan

On User-Level Private Convex Optimization

Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka

GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks

Salah Ghamizi, Jingfeng ZHANG, Maxime Cordy, Mike Papadakis, Masashi Sugiyama

Approximately Optimal Core Shapes for Tensor Decompositions

Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni

Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning

Matthias Gerstgrasser, David C. Parkes

Towards Reliable Neural Specifications

Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, Arie Gurfinkel

Memory-Based Meta-Learning on Non-Stationary Distributions

Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt

Transformers Meet Directed Graphs

Simon Geisler, Yujia Li, Daniel J Mankowitz, Ali Taylan Cemgil, Stephan Günnemann

Cramming: Training a Language Model on a single GPU in one day.

Jonas Geiping, Tom Goldstein

Compositional Score Modeling for Simulation-Based Inference

Tomas Geffner, George Papamakarios, Andriy Mnih

A Reinforcement Learning Framework for Dynamic Mediation Analysis

Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song

On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization

Mudit Gaur, Vaneet Aggarwal, Mridul Agarwal

Federated Heavy Hitter Recovery under Linear Sketching

Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh

Self-supervised learning of Split Invariant Equivariant representations

Quentin Garrido, Laurent Najman, Yann LeCun

RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank

Quentin Garrido, Randall Balestriero, Laurent Najman, Yann LeCun

RLSbench: Domain Adaptation Under Relaxed Label Shift

Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan

The Unreasonable Effectiveness of Few-shot Learning for Machine Translation

Xavier Garcia, Yamini Bansal, Colin Cherry, George Foster, Maxim Krikun

Scaling Laws for Reward Model Overoptimization

Leo Gao, John Schulman, Jacob Hilton

Out-of-Domain Robustness via Targeted Augmentations

Irena Gao, Shiori Sagawa, Pang Wei Koh, Tatsunori Hashimoto, Percy Liang

PAL: Program-aided Language Models

Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu

DDGR: Continual Learning with Deep Diffusion-based Generative Replay

RUI GAO, Weiwei Liu

On the Impact of Algorithmic Recourse on Social Segregation

Ruijiang Gao, Himabindu Lakkaraju

Generalizing Neural Wave Functions

Nicholas Gao, Stephan Günnemann

Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks

Yihang Gao, Yiqi Gu, Michael Ng

Solving Linear Programs with Fast Online Learning Algorithms

Wenzhi Gao, DongDong Ge, Chunlin Sun, Yinyu Ye

Do Perceptually Aligned Gradients Imply Robustness?

Roy Ganz, Bahjat Kawar, Michael Elad