ICML 2023

1,828 papers

TRAK: Attributing Model Behavior at Scale

Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry

Learning Affinity with Hyperbolic Representation for Spatial Propagation

Jin-Hwi Park, Jaesung Choe, Inhwan Bae, Hae-Gon Jeon

Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation

Junyoung Park, Jeongyoun Ahn, Cheolwoo Park

Task-Specific Skill Localization in Fine-tuned Language Models

Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora

Secure Federated Correlation Test and Entropy Estimation

Qi Pang, Lun Wang, Shuai Wang, Wenting Zheng, Dawn Song

Learn to Accumulate Evidence from All Training Samples: Theory and Practice

Deep Shankar Pandey, Qi Yu

Flash: Concept Drift Adaptation in Federated Learning

Kunjal Panchal, Sunav Choudhary, Subrata Mitra, Koyel Mukherjee, Somdeb Sarkhel

Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees.

Ioannis Panageas, Stratis Skoulakis, Luca Viano, Xiao Wang, Volkan Cevher

A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer

Hongyi Pan, Xin Zhu, Salih Furkan Atici, Ahmet Cetin

Better Training of GFlowNets with Local Credit and Incomplete Trajectories

Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio

Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering

ErLin Pan, Zhao Kang

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark

Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart

Controlled Differential Equations on Long Sequences via Non-standard Wavelets

Sourav Pal, Zhanpeng Zeng, Sathya N. Ravi, Vikas Singh

Locally Regularized Neural Differential Equations: Some Black Boxes were meant to remain closed!

Avik Pal, Alan Edelman, Christopher Vincent Rackauckas

Extrapolative Controlled Sequence Generation via Iterative Refinement

Vishakh Padmakumar, Richard Yuanzhe Pang, He He, Ankur P Parikh

Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation

Asuman E. Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang

On the Role of Attention in Prompt-tuning

Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, CHRISTOS THRAMPOULIDIS

Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process

Yidong Ouyang, Liyan Xie, Guang Cheng

Resurrecting Recurrent Neural Networks for Long Sequences

Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre

When does Privileged information Explain Away Label Noise?

Guillermo Ortiz-Jimenez, Mark Collier, Anant Nawalgaria, Alexander Nicholas D’Amour, Jesse Berent

Measuring the Impact of Programming Language Distribution

Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding

Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus

How Many Perturbations Break This Model? Evaluating Robustness Beyond Adversarial Accuracy

Raphael Olivier, Bhiksha Raj

Diffusion Models are Minimax Optimal Distribution Estimators

Kazusato Oko, Shunta Akiyama, Taiji Suzuki

On the Within-Group Fairness of Screening Classifiers

Nastaran Okati, Stratis Tsirtsis, Manuel Gomez Rodriguez

Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality

Guy Ohayon, Theo Joseph Adrai, Michael Elad, Tomer Michaeli

Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances

Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, Liva Ralaivola

Provable Benefit of Mixup for Finding Optimal Decision Boundaries

Junsoo Oh, Chulhee Yun

Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization

Brendan O’Donoghue

Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction

Georgii Sergeevich Novikov, Daniel Bershatsky, Julia Gusak, Alex Shonenkov, Denis Valerievich Dimitrov

CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets

Zachary Novack, Julian McAuley, Zachary Chase Lipton, Saurabh Garg

Gradient-Free Structured Pruning with Unlabeled Data

Azade Nova, Hanjun Dai, Dale Schuurmans

Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling

Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hannaneh Hajishirzi

The Statistical Scope of Multicalibration

Georgy Noarov, Aaron Roth

Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems

Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki

Input Perturbation Reduces Exposure Bias in Diffusion Models

Mang Ning, Enver Sangineto, Angelo Porrello, SIMONE CALDERARA, Rita Cucchiara

Anti-Exploration by Random Network Distillation

Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Sergey Kolesnikov

SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge

Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh

SinFusion: Training Diffusion Models on a Single Image or Video

Yaniv Nikankin, Niv Haim, Michal Irani

A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback

Guanyu Nie, Yididiya Y. Nadew, Yanhui Zhu, Vaneet Aggarwal, Christopher John Quinn

Attributing Image Generative Models using Latent Fingerprints

Guangyu Nie, Changhoon Kim, Yezhou Yang, Yi Ren

Continual Vision-Language Representation Learning with Off-Diagonal Information

Zixuan Ni, Longhui Wei, Siliang Tang, Yueting Zhuang, Qi Tian

LEVER: Learning to Verify Language-to-Code Generation with Execution

Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih

MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL

Fei Ni, Jianye Hao, Yao Mu, Yifu Yuan, YAN ZHENG

Simple Disentanglement of Style and Content in Visual Representations

Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin

Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach

Xuan Son Nguyen, Shuo Yang

Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction

Khai Nguyen, Dang Nguyen, nhật Hồ

Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach

Tri Nguyen, Shahana Ibrahim, Xiao Fu

Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature

Khang Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, nhật Hồ, Stanley Osher

Provable Reset-free Reinforcement Learning by No-Regret Reduction

Hoai-An Nguyen, Ching-An Cheng

ClimaX: A foundation model for weather and climate

Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, Aditya Grover

Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti’s Theorem for Markov Chains

Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh M Halappanavar

Action Matching: Learning Stochastic Dynamics from Samples

Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani

Geometric Autoencoders - What You See is What You Decode

Philipp Nazari, Sebastian Damrich, Fred A Hamprecht

Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

Siddharth Nayak, Kenneth Choi, Wenqi Ding, Sydney Dolan, Karthik Gopalakrishnan

Equivariant Architectures for Learning in Deep Weight Spaces

Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik

On Many-Actions Policy Gradient

Michal Nauman, Marek Cygan

Discovering Object-Centric Generalized Value Functions From Pixels

Somjit Nath, Gopeshh Subbaraj, Khimya Khetarpal, Samira Ebrahimi Kahou

Counterfactual Identifiability of Bijective Causal Models

Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah

Effectively Using Public Data in Privacy Preserving Machine Learning

Milad Nasr, Saeed Mahloujifar, Xinyu Tang, Prateek Mittal, Amir Houmansadr

Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation

Ilan Naiman, Nimrod Berman, Omri Azencot

Do Machine Learning Models Learn Statistical Rules Inferred from Data?

Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong

Statistical Foundations of Prior-Data Fitted Networks

Thomas Nagler

Multi-User Reinforcement Learning with Low Rank Rewards

Dheeraj Mysore Nagaraj, Suhas S Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain

DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization

Adel Nabli, Edouard Oyallon

Representation-Driven Reinforcement Learning

Ofir Nabati, Guy Tennenholtz, Shie Mannor

Dynamical Linear Bandits

Marco Mussi, Alberto Maria Metelli, Marcello Restelli

Efficiently predicting high resolution mass spectra with graph neural networks

Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey

DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning

Tomoya Murata, Taiji Suzuki

GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration

Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka

Uncertain Evidence in Probabilistic Models and Stochastic Simulators

Andreas Munk, Alexander Mead, Frank Wood

Achieving High Accuracy with PINNs via Energy Natural Gradient Descent

Johannes Müller, Marius Zeinhofer

PFNs4BO: In-Context Learning for Bayesian Optimization

Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter

Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models

Matthew J. Muckley, Alaaeldin El-Nouby, Karen Ullrich, Herve Jegou, Jakob Verbeek

Neural signature kernels as infinite-width-depth-limits of controlled ResNets

Nicola Muca Cirone, Maud Lemercier, Cristopher Salvi

Optimistic Planning by Regularized Dynamic Programming

Antoine Moulin, Gergely Neu

ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs

Ted Moskovitz, Brendan O’Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh

WL meet VC

Christopher Morris, Floris Geerts, Jan Tönshoff, Martin Grohe

Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic

Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa

An SDE for Modeling SAM: Theory and Insights

Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting

Compressing Tabular Data via Latent Variable Estimation

Andrea Montanari, Eric Weiner

Input uncertainty propagation through trained neural networks

Paul Monchot, Loic Coquelin, Sébastien Julien Petit, Sebastien Marmin, Erwan Le Pennec

Neural Inverse Operators for Solving PDE Inverse Problems

Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra

Special Properties of Gradient Descent with Large Learning Rates

Amirkeivan Mohtashami, Martin Jaggi, Sebastian U Stich

A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel

Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland

Text-To-Concept (and Back) via Cross-Model Alignment

Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, Soheil Feizi

Pruning via Sparsity-indexed ODE: a Continuous Sparsity Viewpoint

Zhanfeng Mo, Haosen Shi, Sinno Jialin Pan

A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition

Shentong Mo, Pedro Morgado

Disentangled Multiplex Graph Representation Learning

Yujie Mo, Yajie Lei, Jialie Shen, Xiaoshuang Shi, Heng Tao Shen

Diffusion Based Representation Learning

Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D Manning, Chelsea Finn

The Numerical Stability of Hyperbolic Representation Learning

Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang

Optimal Sets and Solution Paths of ReLU Networks

Aaron Mishkin, Mert Pilanci

On the Convergence of Gradient Flow on Multi-layer Linear Models

Hancheng Min, René Vidal, Enrique Mallada

An Information-Theoretic Analysis of Nonstationary Bandit Learning

Seungki Min, Daniel Russo

Directed Chain Generative Adversarial Networks

Ming Min, Ruimeng Hu, Tomoyuki Ichiba

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu

Generative Decoding of Visual Stimuli

Eleni Miliotou, Panagiotis Kyriakis, Jason D Hinman, Andrei Irimia, Paul Bogdan

Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning

Thomas Miconi

Path Neural Networks: Expressive and Accurate Graph Neural Networks

Gaspard Michel, Giannis Nikolentzos, Johannes F. Lutzeyer, Michalis Vazirgiannis