JMLR 2023

381 papers

A Bayesian Bradley-Terry Model to Compare Multiple ML Algorithms on Multiple Data Sets Jacques Wainer

A Complete Characterization of Linear Estimators for Offline Policy Evaluation Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, Sham Kakade

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A Continuous-Time Stochastic Gradient Descent Method for Continuous Data Kexin Jin, Jonas Latz, Chenguang Liu, Carola-Bibiane Schönlieb

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A First Look into the Carbon Footprint of Federated Learning Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro P. B. Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane

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A Framework and Benchmark for Deep Batch Active Learning for Regression David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart

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A General Learning Framework for Open Ad Hoc Teamwork Using Graph-Based Policy Learning Arrasy Rahman, Ignacio Carlucho, Niklas Höpner, Stefano V. Albrecht

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A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi

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A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering Haizi Yu, Igor Mineyev, Lav R. Varshney

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A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin

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A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees Michael J. O'Neill, Stephen J. Wright

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A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits Yasin Abbasi-Yadkori, András György, Nevena Lazić

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A Non-Parametric View of FedAvg and FedProx:Beyond Stationary Points Lili Su, Jiaming Xu, Pengkun Yang

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A Novel Integer Linear Programming Approach for Global L0 Minimization Diego Delle Donne, Matthieu Kowalski, Leo Liberti

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A Parameter-Free Conditional Gradient Method for Composite Minimization Under Hölder Condition Masaru Ito, Zhaosong Lu, Chuan He

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A PDE Approach for Regret Bounds Under Partial Monitoring Erhan Bayraktar, Ibrahim Ekren, Xin Zhang

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A Permutation-Free Kernel Independence Test Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas

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A Randomized Subspace-Based Approach for Dimensionality Reduction and Important Variable Selection Di Bo, Hoon Hwangbo, Vinit Sharma, Corey Arndt, Stephanie TerMaath

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A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs Radu I. Bot, Michael Sedlmayer, Phan Tu Vuong

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A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition Patricia Wollstadt, Sebastian Schmitt, Michael Wibral

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A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers Nahuel Statuto, Irene Unceta, Jordi Nin, Oriol Pujol

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A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan

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A Unified Analysis of Multi-Task Functional Linear Regression Models with Manifold Constraint and Composite Quadratic Penalty Shiyuan He, Hanxuan Ye, Kejun He

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A Unified Approach to Controlling Implicit Regularization via Mirror Descent Haoyuan Sun, Khashayar Gatmiry, Kwangjun Ahn, Navid Azizan

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A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash

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A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee

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A Unified Framework for Optimization-Based Graph Coarsening Manoj Kumar, Anurag Sharma, Sandeep Kumar

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A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds Ben Chugg, Hongjian Wang, Aaditya Ramdas

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A Unified Theory of Diversity in Ensemble Learning Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Luján, Gavin Brown

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Accelerated Primal-Dual Mirror Dynamics for Centralized and Distributed Constrained Convex Optimization Problems You Zhao, Xiaofeng Liao, Xing He, Mingliang Zhou, Chaojie Li

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Adaptation Augmented Model-Based Policy Optimization Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu, Weinan Zhang

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Adaptation to the Range in K-Armed Bandits Hédi Hadiji, Gilles Stoltz

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Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd

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Adaptive Clustering Using Kernel Density Estimators Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann

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Adaptive Data Depth via Multi-Armed Bandits Tavor Baharav, Tze Leung Lai

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Adaptive False Discovery Rate Control with Privacy Guarantee Xintao Xia, Zhanrui Cai

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Adaptive Learning of Density Ratios in RKHS Werner Zellinger, Stefan Kindermann, Sergei V. Pereverzyev

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Alpha-Divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet

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An Analysis of Robustness of Non-Lipschitz Networks Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang

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An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks Stefan Stein, Chenlei Leng

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An Eigenmodel for Dynamic Multilayer Networks Joshua Daniel Loyal, Yuguo Chen

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An Empirical Investigation of the Role of Pre-Training in Lifelong Learning Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell

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An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis

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An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity Wei Liu, Xin Liu, Xiaojun Chen

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Approximate Post-Selective Inference for Regression with the Group LASSO Snigdha Panigrahi, Peter W MacDonald, Daniel Kessler

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Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-Means, and Local Search Benjamin Moseley, Joshua R. Wang

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Asymptotics of Network Embeddings Learned via Subsampling Andrew Davison, Morgane Austern

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Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees Hamid Reza Feyzmahdavian, Mikael Johansson

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Atlas: Few-Shot Learning with Retrieval Augmented Language Models Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave

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Attacks Against Federated Learning Defense Systems and Their Mitigation Cody Lewis, Vijay Varadharajan, Nasimul Noman

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Attribution-Based Explanations That Provide Recourse Cannot Be Robust Hidde Fokkema, Rianne de Heide, Tim van Erven

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Augmented Sparsifiers for Generalized Hypergraph Cuts Nate Veldt, Austin R. Benson, Jon Kleinberg

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Augmented Transfer Regression Learning with Semi-Non-Parametric Nuisance Models Molei Liu, Yi Zhang, Katherine P. Liao, Tianxi Cai

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Autoregressive Networks Binyan Jiang, Jialiang Li, Qiwei Yao

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Bagging in Overparameterized Learning: Risk Characterization and Risk Monotonization Pratik Patil, Jin-Hong Du, Arun Kumar Kuchibhotla

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Bandit Problems with Fidelity Rewards Gábor Lugosi, Ciara Pike-Burke, Pierre-André Savalle

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Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees William J. Wilkinson, Simo Särkkä, Arno Solin

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Bayesian Calibration of Imperfect Computer Models Using Physics-Informed Priors Michail Spitieris, Ingelin Steinsland

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Bayesian Data Selection Eli N. Weinstein, Jeffrey W. Miller

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Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph Leo L. Duan, David B. Dunson

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Bayesian Spiked Laplacian Graphs Leo L Duan, George Michailidis, Mingzhou Ding

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Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders Lisa Bonheme, Marek Grzes

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Benchmarking Graph Neural Networks Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

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Benign Overfitting in Ridge Regression Alexander Tsigler, Peter L. Bartlett

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Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

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Beyond Spectral Gap: The Role of the Topology in Decentralized Learning Thijs Vogels, Hadrien Hendrikx, Martin Jaggi

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Beyond the Golden Ratio for Variational Inequality Algorithms Ahmet Alacaoglu, Axel Böhm, Yura Malitsky

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Bilevel Optimization with a Lower-Level Contraction: Optimal Sample Complexity Without Warm-Start Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

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Boosting Multi-Agent Reinforcement Learning via Contextual Prompting Yue Deng, Zirui Wang, Xi Chen, Yin Zhang

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Buffered Asynchronous SGD for Byzantine Learning Yi-Rui Yang, Wu-Jun Li

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Calibrated Multiple-Output Quantile Regression with Representation Learning Shai Feldman, Stephen Bates, Yaniv Romano

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Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan

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Causal Bandits for Linear Structural Equation Models Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer

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Causal Discovery with Unobserved Confounding and Non-Gaussian Data Y. Samuel Wang, Mathias Drton

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Cluster-Specific Predictions with Multi-Task Gaussian Processes Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey

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Clustering and Structural Robustness in Causal Diagrams Santtu Tikka, Jouni Helske, Juha Karvanen

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Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees Solveig Klepper, Christian Elbracht, Diego Fioravanti, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg

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Combinatorial Optimization and Reasoning with Graph Neural Networks Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Veličković

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Community Models for Networks Observed Through Edge Nominations Tianxi Li, Elizaveta Levina, Ji Zhu

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Community Recovery in the Geometric Block Model Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha

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Comprehensive Algorithm Portfolio Evaluation Using Item Response Theory Sevvandi Kandanaarachchi, Kate Smith-Miles

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Compression, Generalization and Learning Marco C. Campi, Simone Garatti

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Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock

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Concentration Analysis of Multivariate Elliptic Diffusions Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch

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Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data Bingqing Hu, Bin Nan

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Confidence and Uncertainty Assessment for Distributional Random Forests Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann, Nicolai Meinshausen

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Confidence Intervals and Hypothesis Testing for High-Dimensional Quantile Regression: Convolution Smoothing and Debiasing Yibo Yan, Xiaozhou Wang, Riquan Zhang

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Conformal Frequency Estimation Using Discrete Sketched Data with Coverage for Distinct Queries Matteo Sesia, Stefano Favaro, Edgar Dobriban

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Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity Artem Vysogorets, Julia Kempe

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Consistent Model-Based Clustering Using the Quasi-Bernoulli Stick-Breaking Process Cheng Zeng, Jeffrey W Miller, Leo L Duan

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Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks Simge Kucukyavuz, Ali Shojaie, Hasan Manzour, Linchuan Wei, Hao-Hsiang Wu

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Contextual Stochastic Block Model: Sharp Thresholds and Contiguity Chen Lu, Subhabrata Sen

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Continuous-in-Time Limit for Bayesian Bandits Yuhua Zhu, Zachary Izzo, Lexing Ying

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Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency Tetiana Gorbach, Xavier de Luna, Juha Karvanen, Ingeborg Waernbaum

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Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning Titouan Vayer, Rémi Gribonval

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Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning Linxi Liu, Dangna Li, Wing Hung Wong

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Convex Reinforcement Learning in Finite Trials Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli

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DART: Distance Assisted Recursive Testing Xuechan Li, Anthony D. Sung, Jichun Xie

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Decentralized Learning: Theoretical Optimality and Practical Improvements Yucheng Lu, Christopher De Sa

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Decentralized Robust V-Learning for Solving Markov Games with Model Uncertainty Shaocong Ma, Ziyi Chen, Shaofeng Zou, Yi Zhou

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Deep Linear Networks Can Benignly Overfit When Shallow Ones Do Niladri S. Chatterji, Philip M. Long

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Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility Hoil Lee, Fadhel Ayed, Paul Jung, Juho Lee, Hongseok Yang, Francois Caron

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Deletion and Insertion Tests in Regression Models Naofumi Hama, Masayoshi Mase, Art B. Owen

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Densely Connected G-Invariant Deep Neural Networks with Signed Permutation Representations Devanshu Agrawal, James Ostrowski

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Density Estimation on Low-Dimensional Manifolds: An Inflation-Deflation Approach Christian Horvat, Jean-Pascal Pfister

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Differentially Private Hypothesis Testing for Linear Regression Daniel G. Alabi, Salil P. Vadhan

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Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling Stefano Peluchetti

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Dimension Reduction and MARS Yu Liu Liu, Degui Li, Yingcun Xia

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Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection Wenhao Li, Ningyuan Chen, L. Jeff Hong

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Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson

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Dimensionality Reduction and Wasserstein Stability for Kernel Regression Stephan Eckstein, Armin Iske, Mathias Trabs

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Dimensionless Machine Learning: Imposing Exact Units Equivariance Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu

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Discovering Salient Neurons in Deep NLP Models Nadir Durrani, Fahim Dalvi, Hassan Sajjad

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Discrete Variational Calculus for Accelerated Optimization Cédric M. Campos, Alejandro Mahillo, David Martín de Diego

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Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation Christoph Käding, Jakob Runge

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Distributed Algorithms for U-Statistics-Based Empirical Risk Minimization Lanjue Chen, Alan T.K. Wan, Shuyi Zhang, Yong Zhou

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Distributed Community Detection in Large Networks Sheng Zhang, Rui Song, Wenbin Lu, Ji Zhu

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Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-Scale Data Ruoyu Wang, Miaomiao Su, Qihua Wang

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Distributed Sparse Regression via Penalization Yao Ji, Gesualdo Scutari, Ying Sun, Harsha Honnappa

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Distributed Statistical Inference Under Heterogeneity Jia Gu, Song Xi Chen

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Divide-and-Conquer Fusion Ryan S.Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts

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Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning Zihao Li, Boyi Liu, Zhuoran Yang, Zhaoran Wang, Mengdi Wang

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Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence Henry Lam, Haofeng Zhang

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Dropout Training Is Distributionally Robust Optimal José Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang

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Dynamic Ranking with the BTL Model: A Nearest Neighbor Based Rank Centrality Method Eglantine Karlé, Hemant Tyagi

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Efficient Computation of Rankings from Pairwise Comparisons M. E. J. Newman

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Efficient Structure-Preserving Support Tensor Train Machine Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner

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Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net Oskar Allerbo, Johan Jonasson, Rebecka Jörnsten

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Entropic Fictitious Play for Mean Field Optimization Problem Fan Chen, Zhenjie Ren, Songbo Wang

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Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm Louis-Philippe Vignault, Audrey Durand, Pascal Germain

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Escaping the Curse of Dimensionality in Bayesian Model-Based Clustering Noirrit Kiran Chandra, Antonio Canale, David B. Dunson

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Estimating the Carbon Footprint of BLOOM, a 176b Parameter Language Model Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat

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Euler-Lagrange Analysis of Generative Adversarial Networks Siddarth Asokan, Chandra Sekhar Seelamantula

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Evaluating Instrument Validity Using the Principle of Independent Mechanisms Patrick F. Burauel

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Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-Observable Estimators Benjamin Jakubowski, Sriram Somanchi, Edward McFowland Iii, Daniel B. Neill

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Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions Jon Vadillo, Roberto Santana, Jose A. Lozano

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F2A2: Flexible Fully-Decentralized Approximate Actor-Critic for Cooperative Multi-Agent Reinforcement Learning Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha

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Factor Graph Neural Networks Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee

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Fair Data Representation for Machine Learning at the Pareto Frontier Shizhou Xu, Thomas Strohmer

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Faith-Shap: The Faithful Shapley Interaction Index Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar

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Fast Expectation Propagation for Heteroscedastic, Lasso-Penalized, and Quantile Regression Jackson Zhou, John T. Ormerod, Clara Grazian

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Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang

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Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill

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Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation Guillaume Sagnol, Luc Pronzato

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Finding Groups of Cross-Correlated Features in Bi-View Data Miheer Dewaskar, John Palowitch, Mark He, Michael I. Love, Andrew B. Nobel

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Finite-Time Koopman Identifier: A Unified Batch-Online Learning Framework for Joint Learning of Koopman Structure and Parameters Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares

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First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems Michael I. Jordan, Tianyi Lin, Manolis Zampetakis

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Fitting Autoregressive Graph Generative Models Through Maximum Likelihood Estimation Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu

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Flexible Model Aggregation for Quantile Regression Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani

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FLIP: A Utility Preserving Privacy Mechanism for Time Series Tucker McElroy, Anindya Roy, Gaurab Hore

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Foundation Models and Fair Use Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang

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Fourier Neural Operator with Learned Deformations for PDEs on General Geometries Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar

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From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions Johannes Resin

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From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms Weijie Zheng, Benjamin Doerr

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Functional L-Optimality Subsampling for Functional Generalized Linear Models with Massive Data Hua Liu, Jinhong You, Jiguo Cao

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Fundamental Limits and Algorithms for Sparse Linear Regression with Sublinear Sparsity Lan V. Truong

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GANs as Gradient Flows That Converge Yu-Jui Huang, Yuchong Zhang

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Gap Minimization for Knowledge Sharing and Transfer Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu, Gezheng Xu, Christian Gagné, Eric Eaton

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Gaussian Processes with Errors in Variables: Theory and Computation Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll

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Generalization Bounds for Adversarial Contrastive Learning Xin Zou, Weiwei Liu

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Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels Hao Wang, Rui Gao, Flavio P. Calmon

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Generalization Error Bounds for Multiclass Sparse Linear Classifiers Tomer Levy, Felix Abramovich

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Generalized Linear Models in Non-Interactive Local Differential Privacy with Public Data Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu

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Generic Unsupervised Optimization for a Latent Variable Model with Exponential Family Observables Hamid Mousavi, Jakob Drefs, Florian Hirschberger, Jörg Lücke

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GFlowNet Foundations Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio

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Global Convergence of Sub-Gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-Parameterization Jianhao Ma, Salar Fattahi

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Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation Cynthia Rudin, Yaron Shaposhnik

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Graph Attention Retrospective Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath

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Graph Clustering with Graph Neural Networks Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller

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Graph-Aided Online Multi-Kernel Learning Pouya M. Ghari, Yanning Shen

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Group SLOPE Penalized Low-Rank Tensor Regression Yang Chen, Ziyan Luo

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Hard-Constrained Deep Learning for Climate Downscaling Paula Harder, Alex Hernandez-Garcia, Venkatesh Ramesh, Qidong Yang, Prasanna Sattegeri, Daniela Szwarcman, Campbell Watson, David Rolnick

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Hierarchical Kernels in Deep Kernel Learning Wentao Huang, Houbao Lu, Haizhang Zhang

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High-Dimensional Inference for Generalized Linear Models with Hidden Confounding Jing Ouyang, Kean Ming Tan, Gongjun Xu

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Higher-Order Spectral Clustering Under Superimposed Stochastic Block Models Subhadeep Paul, Olgica Milenkovic, Yuguo Chen

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HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation Weijie J. Su, Yuancheng Zhu

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How Do You Want Your Greedy: Simultaneous or Repeated? Moran Feldman, Christopher Harshaw, Amin Karbasi

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Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks Hui Jin, Guido Montufar

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Implicit Regularization and Entrywise Convergence of Riemannian Optimization for Low Tucker-Rank Tensor Completion Haifeng Wang, Jinchi Chen, Ke Wei

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Importance Sparsification for Sinkhorn Algorithm Mengyu Li, Jun Yu, Tao Li, Cheng Meng

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Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning Zhuang Yang

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Improving Multiple-Try Metropolis with Local Balancing Philippe Gagnon, Florian Maire, Giacomo Zanella

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Incremental Learning in Diagonal Linear Networks Raphaël Berthier

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Inference for a Large Directed Acyclic Graph with Unspecified Interventions Chunlin Li, Xiaotong Shen, Wei Pan

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Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds Didong Li, Wenpin Tang, Sudipto Banerjee

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Inference on the Change Point Under a High Dimensional Covariance Shift Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis

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Infinite-Dimensional Optimization and Bayesian Nonparametric Learning of Stochastic Differential Equations Arnab Ganguly, Riten Mitra, Jinpu Zhou

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Insights into Ordinal Embedding Algorithms: A Systematic Evaluation Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg

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Instance-Dependent Confidence and Early Stopping for Reinforcement Learning Eric Xia, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan

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Instance-Dependent Generalization Bounds via Optimal Transport Songyan Hou, Parnian Kassraie, Anastasis Kratsios, Andreas Krause, Jonas Rothfuss

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Integrating Random Effects in Deep Neural Networks Giora Simchoni, Saharon Rosset

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Interpolating Classifiers Make Few Mistakes Tengyuan Liang, Benjamin Recht

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Interpretable and Fair Boolean Rule Sets via Column Generation Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei

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Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang

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Intrinsic Persistent Homology via Density-Based Metric Learning Ximena Fernández, Eugenio Borghini, Gabriel Mindlin, Pablo Groisman

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Iterated Block Particle Filter for High-Dimensional Parameter Learning: Beating the Curse of Dimensionality Ning Ning, Edward L. Ionides

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Jump Interval-Learning for Individualized Decision Making with Continuous Treatments Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu

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Kernel-Based Estimation for Partially Functional Linear Model: Minimax Rates and Randomized Sketches Shaogao Lv, Xin He, Junhui Wang

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Kernel-Matrix Determinant Estimates from Stopped Cholesky Decomposition Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau

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Knowledge Hypergraph Embedding Meets Relational Algebra Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole

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Label Distribution Changing Learning with Sample Space Expanding Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou

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Labels, Information, and Computation: Efficient Learning Using Sufficient Labels Shiyu Duan, Spencer Chang, Jose C. Principe

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LapGym - An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery Paul Maria Scheikl, Balázs Gyenes, Rayan Younis, Christoph Haas, Gerhard Neumann, Martin Wagner, Franziska Mathis-Ullrich

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Large Data Limit of the MBO Scheme for Data Clustering: Convergence of the Dynamics Tim Laux, Jona Lelmi

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Large Sample Spectral Analysis of Graph-Based Multi-Manifold Clustering Nicolas Garcia Trillos, Pengfei He, Chenghui Li

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Leaky Hockey Stick Loss: The First Negatively Divergent Margin-Based Loss Function for Classification Oh-Ran Kwon, Hui Zou

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Learning an Explicit Hyper-Parameter Prediction Function Conditioned on Tasks Jun Shu, Deyu Meng, Zongben Xu

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Learning Conditional Generative Models for Phase Retrieval Tobias Uelwer, Sebastian Konietzny, Alexander Oberstrass, Stefan Harmeling

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Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition Chengzhuo Ni, Yaqi Duan, Munther Dahleh, Mengdi Wang, Anru R. Zhang

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Learning Mean-Field Games with Discounted and Average Costs Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi

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Learning Optimal Feedback Operators and Their Sparse Polynomial Approximations Karl Kunisch, Donato Vásquez-Varas, Daniel Walter

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Learning Optimal Group-Structured Individualized Treatment Rules with Many Treatments Haixu Ma, Donglin Zeng, Yufeng Liu

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Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces George Stepaniants

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Learning to Rank Under Multinomial Logit Choice James A. Grant, David S. Leslie

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Learning-Augmented Count-Min Sketches via Bayesian Nonparametrics Emanuele Dolera, Stefano Favaro, Stefano Peluchetti

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Least Squares Model Averaging for Distributed Data Haili Zhang, Zhaobo Liu, Guohua Zou

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