ICML 2023

1,828 papers

Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes

seyed amir hossein saberi, Amir Najafi, Abolfazl Motahari, Babak Khalaj

Active Ranking of Experts Based on their Performances in Many Tasks

El Mehdi Saad, Nicolas Verzelen, Alexandra Carpentier

Sequential Monte Carlo Learning for Time Series Structure Discovery

Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Yves Rychener, Daniel Kuhn, Tobias Sutter

Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles

Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, CHEN WEI, haoqi fan

SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient

Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov

Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs

Raif M. Rustamov, Subhabrata Majumdar

Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models

Phillip Rust, Anders Søgaard

Constrained Monotonic Neural Networks

Davor Runje, Sharath M Shankaranarayana

Geometric Clifford Algebra Networks

David Ruhe, Jayesh K Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter

A New PHO-rmula for Improved Performance of Semi-Structured Networks

David Rügamer

Function-Space Regularization in Neural Networks: A Probabilistic Perspective

Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson

Infinite Action Contextual Bandits with Reusable Data Exhaust

Mark Rucker, Yinglun Zhu, Paul Mineiro

Robust Satisficing MDPs

Haolin Ruan, Siyu Zhou, Zhi Chen, Chin Pang Ho

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation

Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G Bellemare

Improving Fair Training under Correlation Shifts

Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh

RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents

Rafael Rodriguez-Sanchez, Benjamin Adin Spiegel, Jennifer Wang, Roma Patel, Stefanie Tellex

The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning

Borja Rodrı́guez Gálvez, Arno Blaas, Pau Rodriguez, Adam Golinski, Xavier Suau

Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit

Yeonju Ro, Zhangyang Wang, Vijay Chidambaram, Aditya Akella

A Two-Stage Active Learning Algorithm for k-Nearest Neighbors

Nicholas Rittler, Kamalika Chaudhuri

Multi-Agent Best Arm Identification with Private Communications

Alexandre Rio, Merwan Barlier, Igor Colin, Marta Soare

The Edge of Orthogonality: A Simple View of What Makes BYOL Tick

Pierre Harvey Richemond, Allison Tam, Yunhao Tang, Florian Strub, Bilal Piot

Learning Control-Oriented Dynamical Structure from Data

Spencer M. Richards, Jean-Jacques Slotine, Navid Azizan, Marco Pavone

Run-off Election: Improved Provable Defense against Data Poisoning Attacks

Keivan Rezaei, Kiarash Banihashem, Atoosa Chegini, Soheil Feizi

Feature Programming for Multivariate Time Series Prediction

Alex Daniel Reneau, Jerry Yao-Chieh Hu, Ammar Gilani, Han Liu

Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing

Jiaxiang Ren, Yang Zhou, Jiayin Jin, Lingjuan Lyu, Da Yan

Escaping saddle points in zeroth-order optimization: the power of two-point estimators

Zhaolin Ren, Yujie Tang, Na Li

Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts

Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, Quanshi Zhang

Simplex Random Features

Isaac Reid, Krzysztof Marcin Choromanski, Valerii Likhosherstov, Adrian Weller

Neural networks trained with SGD learn distributions of increasing complexity

Maria Refinetti, Alessandro Ingrosso, Sebastian Goldt

Automated Search for Conjectures on Mathematical Constants using Analysis of Integer Sequences

Ofir Razon, Yoav Harris, Shahar Gottlieb, Dan Carmon, Ofir David

Monotonic Location Attention for Length Generalization

Jishnu Ray Chowdhury, Cornelia Caragea

Beam Tree Recursive Cells

Jishnu Ray Chowdhury, Cornelia Caragea

The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning

Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan Murphy, Finale Doshi-velez

Feature learning in deep classifiers through Intermediate Neural Collapse

Akshay Rangamani, Marius Lindegaard, Tomer Galanti, Tomaso A Poggio

SpENCNN: Orchestrating Encoding and Sparsity for Fast Homomorphically Encrypted Neural Network Inference

Ran Ran, Xinwei Luo, Wei Wang, Tao Liu, Gang Quan

Policy Regularization with Dataset Constraint for Offline Reinforcement Learning

Yuhang Ran, Yi-Chen Li, Fuxiang Zhang, Zongzhang Zhang, Yang Yu

A Picture of the Space of Typical Learnable Tasks

Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh

Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization

Alexandre Rame, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou

How much does Initialization Affect Generalization?

Sameera Ramasinghe, Lachlan Ewen MacDonald, Moshiur Farazi, Hemanth Saratchandran, Simon Lucey

SpotEM: Efficient Video Search for Episodic Memory

Santhosh Kumar Ramakrishnan, Ziad Al-Halah, Kristen Grauman

Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels

Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piche, Bart Dhoedt

Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions

Anant Raj, Lingjiong Zhu, Mert Gurbuzbalaban, Umut Simsekli

Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework

Arman Rahbar, Ashkan Panahi, Morteza Haghir Chehreghani, Devdatt Dubhashi, Hamid Krim

Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series

Aniruddh Raghu, Payal Chandak, Ridwan Alam, John Guttag, Collin Stultz

Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation

Matthew Raffel, Drew Penney, Lizhong Chen

Robust Speech Recognition via Large-Scale Weak Supervision

Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine Mcleavey

DRCFS: Doubly Robust Causal Feature Selection

Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer

Simple and Fast Group Robustness by Automatic Feature Reweighting

Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson

Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search

Xin Qiu, Risto Miikkulainen

Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization

Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang

BiBench: Benchmarking and Analyzing Network Binarization

Haotong Qin, Mingyuan Zhang, Yifu Ding, Aoyu Li, Zhongang Cai

Nugget: Neural Agglomerative Embeddings of Text

Guanghui Qin, Benjamin Van Durme

FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning

Congyu Qiao, Ning Xu, Jiaqi Lv, Yi Ren, Xin Geng

Collaborative Causal Inference with Fair Incentives

Rui Qiao, Xinyi Xu, Bryan Kian Hsiang Low

Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D

Bo Qiang, Yuxuan Song, Minkai Xu, Jingjing Gong, Bowen Gao

An Effective Meaningful Way to Evaluate Survival Models

Shi-ang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan

Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining

Zekun Qi, Runpei Dong, Guofan Fan, Zheng Ge, Xiangyu Zhang

Equivariant Polynomials for Graph Neural Networks

Omri Puny, Derek Lim, Bobak Kiani, Haggai Maron, Yaron Lipman

JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift

Drew Prinster, Suchi Saria, Anqi Liu

Test-time Adaptation with Slot-Centric Models

Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi

Minimax estimation of discontinuous optimal transport maps: The semi-discrete case

Aram-Alexandre Pooladian, Vincent Divol, Jonathan Niles-Weed

Multisample Flow Matching: Straightening Flows with Minibatch Couplings

Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman

Spurious Valleys and Clustering Behavior of Neural Networks

Samuele Pollaci

Hyena Hierarchy: Towards Larger Convolutional Language Models

Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y Fu, Tri Dao

Truncating Trajectories in Monte Carlo Reinforcement Learning

Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli

Sequential Kernelized Independence Testing

Aleksandr Podkopaev, Patrick Blöbaum, Shiva Kasiviswanathan, Aaditya Ramdas

Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data

Lena Podina, Brydon Eastman, Mohammad Kohandel

Conformal Prediction for Federated Uncertainty Quantification Under Label Shift

Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov

Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies

Hannah Pinson, Joeri Lenaerts, Vincent Ginis

HyperTuning: Toward Adapting Large Language Models without Back-propagation

Jason Phang, Yi Mao, Pengcheng He, Weizhu Chen

Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability

Thomy Phan, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Nüßlein

A Scalable Frank-Wolfe-Based Algorithm for the Max-Cut SDP

Chi Bach Pham, Wynita Griggs, James Saunderson

The Power of Learned Locally Linear Models for Nonlinear Policy Optimization

Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu

Certifying Ensembles: A General Certification Theory with S-Lipschitzness

Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip Torr, Adel Bibi

Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation

Luca Pesce, Florent Krzakala, Bruno Loureiro, Ludovic Stephan

Estimating the Contamination Factor’s Distribution in Unsupervised Anomaly Detection

Lorenzo Perini, Paul-Christian Bürkner, Arto Klami

Learning Hidden Markov Models When the Locations of Missing Observations are Unknown

Binyamin Perets, Mark Kozdoba, Shie Mannor

Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation

Andi Peng, Aviv Netanyahu, Mark K Ho, Tianmin Shu, Andreea Bobu

MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation

Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma

The Ideal Continual Learner: An Agent That Never Forgets

Liangzu Peng, Paris Giampouras, René Vidal

Fair Neighbor Embedding

Jaakko Peltonen, Wen Xu, Timo Nummenmaa, Jyrki Nummenmaa

Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation

Gao Peifeng, Qianqian Xu, Peisong Wen, Zhiyong Yang, HuiYang Shao

Dynamics-inspired Neuromorphic Visual Representation Learning

Zhengqi Pei, Shuhui Wang

Can Large Language Models Reason about Program Invariants?

Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin

How Jellyfish Characterise Alternating Group Equivariant Neural Networks

Edward Pearce-Crump

Brauer’s Group Equivariant Neural Networks

Edward Pearce-Crump

Federated Online and Bandit Convex Optimization

Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro

Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs

Saro Passaro, C. Lawrence Zitnick

Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network

Farhad Pashakhanloo, Alexei Koulakov

PAC Generalization via Invariant Representations

Advait U Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai

Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators

Paavo Parmas, Takuma Seno, Yuma Aoki

Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations

Jisun Park, Ernest K. Ryu

Neural Stochastic Differential Games for Time-series Analysis

Sungwoo Park, Byoungwoo Park, Moontae Lee, Changhee Lee

Predictable MDP Abstraction for Unsupervised Model-Based RL

Seohong Park, Sergey Levine

Controllability-Aware Unsupervised Skill Discovery

Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel

Differentially Private Sharpness-Aware Training

Jinseong Park, Hoki Kim, Yujin Choi, Jaewook Lee

Learning Controllable Degradation for Real-World Super-Resolution via Constrained Flows

Seobin Park, Dongjin Kim, Sungyong Baik, Tae Hyun Kim

Towards Understanding Ensemble Distillation in Federated Learning

Sejun Park, Kihun Hong, Ganguk Hwang

Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization

Jungwuk Park, Dong-Jun Han, Soyeong Kim, Jaekyun Moon