ICML 2023

1,828 papers

Learning Instance-Specific Augmentations by Capturing Local Invariances

Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh

Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function

Elissa Mhanna, Mohamad Assaad

Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL

Zakaria Mhammedi, Dylan J Foster, Alexander Rakhlin

MANSA: Learning Fast and Slow in Multi-Agent Systems

David Henry Mguni, Haojun Chen, Taher Jafferjee, Jianhong Wang, Longfei Yue

Training Deep Surrogate Models with Large Scale Online Learning

Lucas Thibaut Meyer, Marc Schouler, Robert Alexander Caulk, Alejandro Ribes, Bruno Raffin

Quantum Policy Gradient Algorithm with Optimized Action Decoding

Nico Meyer, Daniel Scherer, Axel Plinge, Christopher Mutschler, Michael Hartmann

Towards Theoretical Understanding of Inverse Reinforcement Learning

Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli

Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks?

Dmitry Metelev, Alexander Rogozin, Dmitry Kovalev, Alexander Gasnikov

Quantile Credit Assignment

Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland

On Preemption and Learning in Stochastic Scheduling

Nadav Merlis, Hugo Richard, Flore Sentenac, Corentin Odic, Mathieu Molina

Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning

Yu Meng, Martin Michalski, Jiaxin Huang, Yu Zhang, Tarek Abdelzaher

A Model-Based Method for Minimizing CVaR and Beyond

Si Yi Meng, Robert M. Gower

Superhuman Fairness

Omid Memarrast, Linh Vu, Brian D Ziebart

Reprogramming Pretrained Language Models for Antibody Sequence Infilling

Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar

Normalizing Flows for Interventional Density Estimation

Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel

Stochastic Gradient Succeeds for Bandits

Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári

A Model-free Closeness-of-influence Test for Features in Supervised Learning

Mohammad Mehrabi, Ryan A. Rossi

Robustness in Multimodal Learning under Train-Test Modality Mismatch

Brandon McKinzie, Vaishaal Shankar, Joseph Yitan Cheng, Yinfei Yang, Jonathon Shlens

PAC-Bayesian Generalization Bounds for Adversarial Generative Models

Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain

Nonparametric Density Estimation under Distribution Drift

Alessio Mazzetto, Eli Upfal

Communication-Constrained Bandits under Additive Gaussian Noise

Prathamesh Mayekar, Jonathan Scarlett, Vincent Y. F. Tan

Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry

Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef Marzouk

Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation

Aditya Mate, Bryan Wilder, Aparna Taneja, Milind Tambe

Generative Pretraining for Black-Box Optimization

Satvik Mehul Mashkaria, Siddarth Krishnamoorthy, Aditya Grover

Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective

Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski

Computational Asymmetries in Robust Classification

Samuele Marro, Michele Lombardi

Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification

Juan Maroñas, Daniel Hernández-lobato

Quantized Distributed Training of Large Models with Convergence Guarantees

Ilia Markov, Adrian Vladu, Qi Guo, Dan Alistarh

Analyzing Diffusion as Serial Reproduction

Raja Marjieh, Ilia Sucholutsky, Thomas A Langlois, Nori Jacoby, Thomas L. Griffiths

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts

Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados

Evaluating Unsupervised Denoising Requires Unsupervised Metrics

Adria Marcos Morales, Matan Leibovich, Sreyas Mohan, Joshua Lawrence Vincent, Piyush Haluai

Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal

Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, SIMONE CALDERARA, Andrea Passerini

SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning.

Tanguy Marchand, Regis Loeb, Ulysse Marteau-Ferey, Jean Ogier du Terrail, Arthur Pignet

Reliable Measures of Spread in High Dimensional Latent Spaces

Anna Marbut, Katy Mckinney-Bock, Travis Wheeler

Robust Perception through Equivariance

Chengzhi Mao, Lingyu Zhang, Abhishek Vaibhav Joshi, Junfeng Yang, Hao Wang

Supported Trust Region Optimization for Offline Reinforcement Learning

Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji

Cross-Entropy Loss Functions: Theoretical Analysis and Applications

Anqi Mao, Mehryar Mohri, Yutao Zhong

H-Consistency Bounds for Pairwise Misranking Loss Surrogates

Anqi Mao, Mehryar Mohri, Yutao Zhong

Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice

Yishay Mansour, Richard Nock, Robert Williamson

Differential Privacy has Bounded Impact on Fairness in Classification

Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi

Performative Reinforcement Learning

Debmalya Mandal, Stelios Triantafyllou, Goran Radanovic

A Kernel-Based View of Language Model Fine-Tuning

Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora

Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality

Dhruv Malik, Conor Igoe, Yuanzhi Li, Aarti Singh

Additive Causal Bandits with Unknown Graph

Alan Malek, Virginia Aglietti, Silvia Chiappa

Fundamental Tradeoffs in Learning with Prior Information

Anirudha Majumdar

Can Neural Network Memorization Be Localized?

Pratyush Maini, Michael Curtis Mozer, Hanie Sedghi, Zachary Chase Lipton, J Zico Kolter

Vertical Federated Graph Neural Network for Recommender System

Peihua Mai, Yan Pang

CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations

Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon

Applied Online Algorithms with Heterogeneous Predictors

Jessica Maghakian, Russell Lee, Mohammad Hajiesmaili, Jian Li, Ramesh Sitaraman

Learning GFlowNets From Partial Episodes For Improved Convergence And Stability

Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain

AutoCoreset: An Automatic Practical Coreset Construction Framework

Alaa Maalouf, Murad Tukan, Vladimir braverman, Daniela Rus

Calibrating Multimodal Learning

Huan Ma, Qingyang Zhang, Changqing Zhang, Bingzhe Wu, Huazhu Fu

Generated Graph Detection

Yihan Ma, Zhikun Zhang, Ning Yu, Xinlei He, Michael Backes

Buying Information for Stochastic Optimization

Mingchen Ma, Christos Tzamos

Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points

Ziye Ma, Igor Molybog, Javad Lavaei, Somayeh Sojoudi

Learning Intuitive Policies Using Action Features

Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Nicolaus Foerster

Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping

Baorui Ma, Yu-Shen Liu, Zhizhong Han

Graph Inductive Biases in Transformers without Message Passing

Liheng Ma, CHEN LIN, Derek Lim, Adriana Romero-Soriano, Puneet K. Dokania

LIV: Language-Image Representations and Rewards for Robotic Control

Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman

Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics

Pingchuan Ma, Peter Yichen Chen, Bolei Deng, Joshua B. Tenenbaum, Tao Du

Which Tricks are Important for Learning to Rank?

Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova

Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions

Boxiang Lyu, Zhe Feng, Zachary Robertson, Sanmi Koyejo

Bandits with Knapsacks: Advice on Time-Varying Demands

Lixing Lyu, Wang Chi Cheung

Understanding Plasticity in Neural Networks

Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Avila Pires, Razvan Pascanu

DiscoBAX: Discovery of optimal intervention sets in genomic experiment design

Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer

OCD: Learning to Overfit with Conditional Diffusion Models

Shahar Lutati, Lior Wolf

Stabilizing GANs’ Training with Brownian Motion Controller

Tianjiao Luo, Ziyu Zhu, Jianfei Chen, Jun Zhu

HOPE: High-order Graph ODE For Modeling Interacting Dynamics

Xiao Luo, Jingyang Yuan, Zijie Huang, Huiyu Jiang, Yifang Qin

A Closer Look at Few-shot Classification Again

Xu Luo, Hao Wu, Ji Zhang, Lianli Gao, Jing Xu

Iterative Approximate Cross-Validation

Yuetian Luo, Zhimei Ren, Rina Barber

Dimensionality Reduction for General KDE Mode Finding

Xinyu Luo, Christopher Musco, Cas Widdershoven

Image Restoration with Mean-Reverting Stochastic Differential Equations

Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön

SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation

Huaishao Luo, Junwei Bao, Youzheng Wu, Xiaodong He, Tianrui Li

A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions

Daniel Lundstrom, Meisam Razaviyayn

Mechanistic Mode Connectivity

Ekdeep Singh Lubana, Eric J Bigelow, Robert P. Dick, David Krueger, Hidenori Tanaka

Federated Conformal Predictors for Distributed Uncertainty Quantification

Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael Jordan, Ramesh Raskar

Adversarial Cheap Talk

Chris Lu, Timon Willi, Alistair Letcher, Jakob Nicolaus Foerster

Learning Dense Correspondences between Photos and Sketches

Xuanchen Lu, Xiaolong Wang, Judith E Fan

QAS-Bench: Rethinking Quantum Architecture Search and A Benchmark

Xudong Lu, Kaisen Pan, Ge Yan, Jiaming Shan, Wenjie Wu

Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks

Yiwei Lu, Gautam Kamath, Yaoliang Yu

Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning

Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li

STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition

Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa

Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective

Yulong Lu

Bilevel Optimization with Coupled Decision-Dependent Distributions

Songtao Lu

Never mind the metrics---what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective

David Lovell, Dimity Miller, Jaiden Capra, Andrew P. Bradley

Reflected Diffusion Models

Aaron Lou, Stefano Ermon

Dataset Distillation with Convexified Implicit Gradients

Noel Loo, Ramin Hasani, Mathias Lechner, Daniela Rus

The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung

Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries

Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu

Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning

Sam Lobel, Akhil Bagaria, George Konidaris

Contextual Combinatorial Bandits with Probabilistically Triggered Arms

Xutong Liu, Jinhang Zuo, Siwei Wang, John C.S. Lui, Mohammad Hajiesmaili

NA^2Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning

Zichuan Liu, Yuanyang Zhu, Chunlin Chen

Unsupervised Out-of-Distribution Detection with Diffusion Inpainting

Zhenzhen Liu, Jin Peng Zhou, Yufan Wang, Kilian Q Weinberger

Which Invariance Should We Transfer? A Causal Minimax Learning Approach

Mingzhou Liu, Xiangyu Zheng, Xinwei Sun, Fang Fang, Yizhou Wang

Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression

Zhuoran Liu, Zhengyu Zhao, Martha Larson

D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching

Xuanzhou Liu, Lin Zhang, Jiaqi Sun, Yujiu Yang, Haiqin Yang

Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning

Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu

Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing

Xiangyu Liu, Kaiqing Zhang

VectorMapNet: End-to-end Vectorized HD Map Learning

Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, Hang Zhao

Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space

Weitang Liu, Yi-Zhuang You, Ying Wai Li, Jingbo Shang