ICML 2023

1,828 papers

Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions

Wanshan Li, Daren Wang, Alessandro Rinaldo

Hierarchical Diffusion for Offline Decision Making

Wenhao Li, Xiangfeng Wang, Bo Jin, Hongyuan Zha

Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling

Yunfan Li, Yiran Wang, Yu Cheng, Lin Yang

Efficient Quantum Algorithms for Quantum Optimal Control

Xiantao Li, Chunhao Wang

How Powerful are Shallow Neural Networks with Bandlimited Random Weights?

Ming Li, Sho Sonoda, Feilong Cao, Yu Guang Wang, Jiye Liang

Federated Adversarial Learning: A Framework with Convergence Analysis

Xiaoxiao Li, Zhao Song, Jiaming Yang

On the Initialization of Graph Neural Networks

Jiahang Li, Yakun Song, Xiang Song, David Wipf

Deep Anomaly Detection under Labeling Budget Constraints

Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks

Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan

Reconstructive Neuron Pruning for Backdoor Defense

Yige Li, Xixiang Lyu, Xingjun Ma, Nodens Koren, Lingjuan Lyu

Optimal Convergence Rates for Agnostic Nyström Kernel Learning

Jian Li, Yong Liu, Weiping Wang

Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction

Shaojie Li, Yong Liu

Revisiting Weighted Aggregation in Federated Learning with Neural Networks

Zexi Li, Tao LIN, Xinyi Shang, Chao Wu

Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression

Junfan Li, Shizhong Liao

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Junnan Li, DONGXU LI, Silvio Savarese, Steven Hoi

How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding

Yuchen Li, Yuanzhi Li, Andrej Risteski

Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation

Xiaoyun Li, Ping Li

Local Vertex Colouring Graph Neural Networks

Shouheng Li, Dongwoo Kim, Qing Wang

Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models

Rui Li, S. T. John, Arno Solin

Transformers as Algorithms: Generalization and Stability in In-context Learning

Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, Samet Oymak

Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints

Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang

Adversarial Collaborative Learning on Non-IID Features

Qinbin Li, Bingsheng He, Dawn Song

RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution

Pengyi Li, Jianye Hao, Hongyao Tang, YAN ZHENG, Xian Fu

FAIRER: Fairness as Decision Rationale Alignment

Tianlin Li, Qing Guo, Aishan Liu, Mengnan Du, Zhiming Li

Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation

Li’Ang Li, Yifei Duan, Guanghua Ji, Yongqiang Cai

Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation

Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal

Learning Preconditioners for Conjugate Gradient PDE Solvers

Yichen Li, Peter Yichen Chen, Tao Du, Wojciech Matusik

Prototype-oriented unsupervised anomaly detection for multivariate time series

Yuxin Li, wenchao chen, Bo Chen, Dongsheng Wang, Long Tian

Internet Explorer: Targeted Representation Learning on the Open Web

Alexander Cong Li, Ellis Langham Brown, Alexei A Efros, Deepak Pathak

MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations

Anqi Li, Byron Boots, Ching-An Cheng

TIPS: Topologically Important Path Sampling for Anytime Neural Networks

Guihong Li, Kartikeya Bhardwaj, Yuedong Yang, Radu Marculescu

GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanations

Dan Ley, Saumitra Mishra, Daniele Magazzeni

Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation

Orin Levy, Alon Cohen, Asaf Cassel, Yishay Mansour

Fast Inference from Transformers via Speculative Decoding

Yaniv Leviathan, Matan Kalman, Yossi Matias

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault-Levasseur

Learning Control by Iterative Inversion

Gal Leibovich, Guy Jacob, Or Avner, Gal Novik, Aviv Tamar

Generalization Analysis for Contrastive Representation Learning

Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou

Unsupervised Skill Discovery for Learning Shared Structures across Changing Environments

Sang-Hyun Lee, Seung-Woo Seo

Implicit Jacobian regularization weighted with impurity of probability output

Sungyoon Lee, Jinseong Park, Jaewook Lee

On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters

Wonyeol Lee, Sejun Park, Alex Aiken

Demystifying Disagreement-on-the-Line in High Dimensions

Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani

QASA: Advanced Question Answering on Scientific Articles

Yoonjoo Lee, Kyungjae Lee, Sunghyun Park, Dasol Hwang, Jaehyeon Kim

HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption

Seewoo Lee, Garam Lee, Jung woo Kim, Junbum Shin, Mun-Kyu Lee

On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning

Hojoon Lee, Koanho Lee, Dongyoon Hwang, Hyunho Lee, Byungkun Lee

H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features

Hangbin Lee, Youngjo Lee

Minimizing Trajectory Curvature of ODE-based Generative Models

Sangyun Lee, Beomsu Kim, Jong Chul Ye

CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis

Chaejeong Lee, Jayoung Kim, Noseong Park

FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization

Jung Hyun Lee, Jeonghoon Kim, Se Jung Kwon, Dongsoo Lee

Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding

Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu

Exploring Chemical Space with Score-based Out-of-distribution Generation

Seul Lee, Jaehyeong Jo, Sung Ju Hwang

Conditional Graph Information Bottleneck for Molecular Relational Learning

Namkyeong Lee, Dongmin Hyun, Gyoung S. Na, Sungwon Kim, Junseok Lee

Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits

Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama

InGram: Inductive Knowledge Graph Embedding via Relation Graphs

Jaejun Lee, Chanyoung Chung, Joyce Jiyoung Whang

Towards Deep Attention in Graph Neural Networks: Problems and Remedies

Soo Yong Lee, Fanchen Bu, Jaemin Yoo, Kijung Shin

Learning in POMDPs is Sample-Efficient with Hindsight Observability

Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang

Strategic Classification with Unknown User Manipulations

Tosca Lechner, Ruth Urner, Shai Ben-David

Bootstrapped Representations in Reinforcement Learning

Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal

Pre-training for Speech Translation: CTC Meets Optimal Transport

Phuong-Hang Le, Hongyu Gong, Changhan Wang, Juan Pino, Benjamin Lecouteux

Cluster Explanation via Polyhedral Descriptions

Connor Lawless, Oktay Gunluk

Target-based Surrogates for Stochastic Optimization

Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux

Who Needs to Know? Minimal Knowledge for Optimal Coordination

Niklas Lauffer, Ameesh Shah, Micah Carroll, Michael D Dennis, Stuart Russell

Speeding Up Bellman Ford via Minimum Violation Permutations

Silvio Lattanzi, Ola Svensson, Sergei Vassilvitskii

Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints

Alexandra Anna Lassota, Alexander Lindermayr, Nicole Megow, Jens Schlöter

On the Occupancy Measure of Non-Markovian Policies in Continuous MDPs

Romain Laroche, Remi Tachet des Combes

Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability

Robert Tjarko Lange, Henning Sprekeler

Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback

Tal Lancewicki, Aviv Rosenberg, Dmitry Sotnikov

Metagenomic Binning using Connectivity-constrained Variational Autoencoders

Andre Lamurias, Alessandro Tibo, Katja Hose, Mads Albertsen, Thomas Dyhre Nielsen

LegendreTron: Uprising Proper Multiclass Loss Learning

Kevin H Lam, Christian Walder, Spiridon Penev, Richard Nock

Bootstrap in High Dimension with Low Computation

Henry Lam, Zhenyuan Liu

Private Statistical Estimation of Many Quantiles

Clément Lalanne, Aurélien Garivier, Remi Gribonval

FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation

Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji

ChiPFormer: Transferable Chip Placement via Offline Decision Transformer

Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, Jianye Hao

DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation

Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong

Automatically marginalized MCMC in probabilistic programming

Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon

A theory of continuous generative flow networks

Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova

Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection

Marc Lafon, Elias Ramzi, Clément Rambour, Nicolas Thome

Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs

Steinar Laenen, Bogdan Adrian Manghiuc, He Sun

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning

Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio

Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning

Aqeel Labash, Florian Stelzer, Daniel Majoral, Raul Vicente Zafra

Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value

Yongchan Kwon, James Zou

Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning

Dohyun Kwon, Hanbaek Lyu

A Fully First-Order Method for Stochastic Bilevel Optimization

Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D Nowak

Reward-Mixing MDPs with Few Latent Contexts are Learnable

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Rotation and Translation Invariant Representation Learning with Implicit Neural Representations

Sehyun Kwon, Joo Young Choi, Ernest K. Ryu

GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency

Min-Seop Kwak, Jiuhn Song, Seungryong Kim

Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders

Oskar Kviman, Ricky Molén, Alexandra Hotti, Semih Kurt, Vı́ctor Elvira

Tied-Augment: Controlling Representation Similarity Improves Data Augmentation

Emirhan Kurtuluş, Zichao Li, Yann Dauphin, Ekin Dogus Cubuk

Modeling Dynamic Environments with Scene Graph Memory

Andrey Kurenkov, Michael Lingelbach, Tanmay Agarwal, Emily Jin, Chengshu Li

Featured Graph Coarsening with Similarity Guarantees

Manoj Kumar, Anurag Sharma, Shashwat Saxena, Sandeep Kumar

Towards Explaining Distribution Shifts

Sean Kulinski, David I. Inouye

SinDDM: A Single Image Denoising Diffusion Model

Vladimir Kulikov, Shahar Yadin, Matan Kleiner, Tomer Michaeli

Hierarchical Imitation Learning with Vector Quantized Models

Kalle Kujanpää, Joni Pajarinen, Alexander Ilin

Variance Control for Distributional Reinforcement Learning

Qi Kuang, Zhoufan Zhu, Liwen Zhang, Fan Zhou

Learning to Design Analog Circuits to Meet Threshold Specifications

Dmitrii Krylov, Pooya Khajeh, Junhan Ouyang, Thomas Reeves, Tongkai Liu

Diffusion Models for Black-Box Optimization

Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover

Graph Neural Tangent Kernel: Convergence on Large Graphs

Sanjukta Krishnagopal, Luana Ruiz

Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten

Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju

Multi-Task Differential Privacy Under Distribution Skew

Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta

Estimation Beyond Data Reweighting: Kernel Method of Moments

Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu

Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond

Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon