ICML 2023

1,828 papers

Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss

Pierre Bréchet, Katerina Papagiannouli, Jing An, Guido Montúfar

Quantifying Human Priors over Social and Navigation Networks

Gecia Bravo-Hermsdorff

Provably Learning Object-Centric Representations

Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen

ILLUME: Rationalizing Vision-Language Models through Human Interactions

Manuel Brack, Patrick Schramowski, Björn Deiseroth, Kristian Kersting

Settling the Reward Hypothesis

Michael Bowling, John D Martin, David Abel, Will Dabney

Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?

Victor Boutin, Thomas FEL, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj

Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling

Adam Bouland, Yosheb M Getachew, Yujia Jin, Aaron Sidford, Kevin Tian

In Search for a Generalizable Method for Source Free Domain Adaptation

Malik Boudiaf, Tom Denton, Bart van Merrienboer, Vincent Dumoulin, Eleni Triantafillou

Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge

Shahine Bouabid, Jake Fawkes, Dino Sejdinovic

Model-agnostic Measure of Generalization Difficulty

Akhilan Boopathy, Kevin Liu, Jaedong Hwang, Shu Ge, Asaad Mohammedsaleh

The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning

Victor Boone, Bruno Gaujal

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust

Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals

Clément Bonet, Benoı̂t Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau

Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels

Simone Bombari, Shayan Kiyani, Marco Mondelli

A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks

David Boetius, Stefan Leue, Tobias Sutter

Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist

Niclas Boehmer, Piotr Faliszewski, Sonja Kraiczy

Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions

Niclas Boehmer, L. Elisa Celis, Lingxiao Huang, Anay Mehrotra, Nisheeth K. Vishnoi

Learning the Dynamics of Sparsely Observed Interacting Systems

Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot, Agathe Guilloux

Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits

Markus Bläser

FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems

Matthieu Blanke, marc lelarge

Unit Scaling: Out-of-the-Box Low-Precision Training

Charlie Blake, Douglas Orr, Carlo Luschi

Understanding Oversquashing in GNNs through the Lens of Effective Resistance

Mitchell Black, Zhengchao Wan, Amir Nayyeri, Yusu Wang

Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames

Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin Fathy Elsayed, Aravindh Mahendran

In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation

Julian Bitterwolf, Maximilian Müller, Matthias Hein

Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion

Marin Biloš, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann

StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes

Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu, N M Anoop Krishnan

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien

Data-Copying in Generative Models: A Formal Framework

Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri

Improved Online Conformal Prediction via Strongly Adaptive Online Learning

Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai

Bandit Online Linear Optimization with Hints and Queries

Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference

Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, François-Xavier Briol

Neural Algorithmic Reasoning with Causal Regularisation

Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini

Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks

Louis Béthune, Paul Novello, Guillaume Coiffier, Thibaut Boissin, Mathieu Serrurier

Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision

Arturs Berzins

Differentiable and Transportable Structure Learning

Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

Constrained Phi-Equilibria

Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Francesco Trovò, Nicola Gatti

Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion

Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Francesco Trovò

Gaussian processes at the Helm(holtz): A more fluid model for ocean currents

Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan

Certified Robust Neural Networks: Generalization and Corruption Resistance

Amine Bennouna, Ryan Lucas, Bart Van Parys

On Second-Order Scoring Rules for Epistemic Uncertainty Quantification

Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

Controllable Neural Symbolic Regression

Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny

Continuously Parameterized Mixture Models

Christopher M Bender, Yifeng Shi, Marc Niethammer, Junier Oliva

Fast as CHITA: Neural Network Pruning with Combinatorial Optimization

Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva

TIDE: Time Derivative Diffusion for Deep Learning on Graphs

Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov

Explaining Reinforcement Learning with Shapley Values

Daniel Beechey, Thomas M. S. Smith, Ozgur Simsek

Predicting Ordinary Differential Equations with Transformers

Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus

Individually Fair Learning with One-Sided Feedback

Yahav Bechavod, Aaron Roth

A Kernel Stein Test of Goodness of Fit for Sequential Models

Jerome Baum, Heishiro Kanagawa, Arthur Gretton

Human-Timescale Adaptation in an Open-Ended Task Space

Jakob Bauer, Kate Baumli, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg

A Statistical Perspective on Retrieval-Based Models

Soumya Basu, Ankit Singh Rawat, Manzil Zaheer

User-level Private Stochastic Convex Optimization with Optimal Rates

Raef Bassily, Ziteng Sun

Moccasin: Efficient Tensor Rematerialization for Neural Networks

Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina

Interpretable Neural-Symbolic Concept Reasoning

Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister

Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space

Anas Barakat, Ilyas Fatkhullin, Niao He

MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation

Omer Bar-Tal, Lior Yariv, Yaron Lipman, Tali Dekel

Optimizing the Collaboration Structure in Cross-Silo Federated Learning

Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He

One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale

Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu

Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time

Kiarash Banihashem, Leyla Biabani, Samira Goudarzi, MohammadTaghi Hajiaghayi, Peyman Jabbarzade

Robust Budget Pacing with a Single Sample

Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang

On the Functional Similarity of Robust and Non-Robust Neural Representations

András Balogh, Márk Jelasity

Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes

Marin Ballu, Quentin Berthet

Efficient Online Reinforcement Learning with Offline Data

Philip J. Ball, Laura Smith, Ilya Kostrikov, Sergey Levine

Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures

Oleg Balabanov, Matthias Beaupère, Laura Grigori, Victor Lederer

Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems

Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau

Implicit Graph Neural Networks: A Monotone Operator Viewpoint

Justin Baker, Qingsong Wang, Cory D Hauck, Bao Wang

Linear optimal partial transport embedding

Yikun Bai, Ivan Vladimir Medri, Rocio Diaz Martin, Rana Shahroz, Soheil Kolouri

Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization

Yushi Bai, Xin Lv, Juanzi Li, Lei Hou

Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection

Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D Nowak

Efficient preconditioned stochastic gradient descent for estimation in latent variable models

Charlotte Baey, Maud Delattre, Estelle Kuhn, Jean-Benoist Leger, Sarah Lemler

Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language

Alexei Baevski, Arun Babu, Wei-Ning Hsu, Michael Auli

Personalized Subgraph Federated Learning

Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, Sung Ju Hwang

CLUTR: Curriculum Learning via Unsupervised Task Representation Learning

Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis, Aleksandra Faust

Half-Hop: A graph upsampling approach for slowing down message passing

Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi

Naive imputation implicitly regularizes high-dimensional linear models

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Gradient Descent Converges Linearly for Logistic Regression on Separable Data

Kyriakos Axiotis, Maxim Sviridenko

Dirichlet Diffusion Score Model for Biological Sequence Generation

Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, Jian Zhou

On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm

Julien Aubert, Luc Lehéricy, Patricia Reynaud-Bouret

Learning to Initiate and Reason in Event-Driven Cascading Processes

Yuval Atzmon, Eli Meirom, Shie Mannor, Gal Chechik

Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning

Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas

Adversarially Robust PAC Learnability of Real-Valued Functions

Idan Attias, Steve Hanneke

SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance

Amit Attia, Tomer Koren

From Robustness to Privacy and Back

Hilal Asi, Jonathan Ullman, Lydia Zakynthinou

Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime

Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar

Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning

Nader Asadi, MohammadReza Davari, Sudhir Mudur, Rahaf Aljundi, Eugene Belilovsky

Faster Rates of Convergence to Stationary Points in Differentially Private Optimization

Raman Arora, Raef Bassily, Tomas Gonzalez, Cristóbal A Guzmán, Michael Menart

Principled Acceleration of Iterative Numerical Methods Using Machine Learning

Sohei Arisaka, Qianxiao Li

Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models

Jamil Arbas, Hassan Ashtiani, Christopher Liaw

Exponential Smoothing for Off-Policy Learning

Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba

Mixing Predictions for Online Metric Algorithms

Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, Bertrand Simon

Paging with Succinct Predictions

Antonios Antoniadis, Joan Boyar, Marek Elias, Lene Monrad Favrholdt, Ruben Hoeksma

Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series

Abdul Fatir Ansari, Alvin Heng, Andre Lim, Harold Soh

SGD with Large Step Sizes Learns Sparse Features

Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion

A Modern Look at the Relationship between Sharpness and Generalization

Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion

Near-Optimal $Φ$-Regret Learning in Extensive-Form Games

Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm

Meta Optimal Transport

Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko

The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation

Philip Amortila, Nan Jiang, Csaba Szepesvári

A Kernelized Stein Discrepancy for Biological Sequences

Alan Nawzad Amin, Eli N Weinstein, Debora Susan Marks

Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost

Sanae Amani, Tor Lattimore, András György, Lin Yang

Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels

Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl

Robust and Scalable Bayesian Online Changepoint Detection

Matias Altamirano, François-Xavier Briol, Jeremias Knoblauch