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JMLR Volume 24

Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search: Benjamin Moseley, Joshua R. Wang; (1):1−36, 2023.
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The Brier Score under Administrative Censoring: Problems and a Solution: Håvard Kvamme, Ørnulf Borgan; (2):1−26, 2023.
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Bayesian Spiked Laplacian Graphs: Leo L Duan, George Michailidis, Mingzhou Ding; (3):1−35, 2023.
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Efficient Structure-preserving Support Tensor Train Machine: Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner; (4):1−22, 2023.
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Cluster-Specific Predictions with Multi-Task Gaussian Processes: Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey; (5):1−49, 2023.
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AutoKeras: An AutoML Library for Deep Learning: Haifeng Jin, François Chollet, Qingquan Song, Xia Hu; (6):1−6, 2023. (Machine Learning Open Source Software Paper)
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On Distance and Kernel Measures of Conditional Dependence: Tianhong Sheng, Bharath K. Sriperumbudur; (7):1−16, 2023.
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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; (8):1−37, 2023.
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Sampling random graph homomorphisms and applications to network data analysis: Hanbaek Lyu, Facundo Memoli, David Sivakoff; (9):1−79, 2023.
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A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees: Michael J. O'Neill, Stephen J. Wright; (10):1−34, 2023.
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Optimal Strategies for Reject Option Classifiers: Vojtech Franc, Daniel Prusa, Vaclav Voracek; (11):1−49, 2023.
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Learning-augmented count-min sketches via Bayesian nonparametrics: Emanuele Dolera, Stefano Favaro, Stefano Peluchetti; (12):1−60, 2023.
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Adaptation to the Range in K-Armed Bandits: Hédi Hadiji, Gilles Stoltz; (13):1−33, 2023.
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Python package for causal discovery based on LiNGAM: Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu; (14):1−8, 2023. (Machine Learning Open Source Software Paper)
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Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions: Jon Vadillo, Roberto Santana, Jose A. Lozano; (15):1−42, 2023.
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Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation: Cynthia Rudin, Yaron Shaposhnik; (16):1−44, 2023.
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Learning Mean-Field Games with Discounted and Average Costs: Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi; (17):1−59, 2023.
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An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization: Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis; (18):1−41, 2023.
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Regularized Joint Mixture Models: Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee; (19):1−47, 2023.
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Interpolating Classifiers Make Few Mistakes: Tengyuan Liang, Benjamin Recht; (20):1−27, 2023.
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Graph-Aided Online Multi-Kernel Learning: Pouya M. Ghari, Yanning Shen; (21):1−44, 2023.
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Lower Bounds and Accelerated Algorithms for Bilevel Optimization: Kaiyi ji, Yingbin Liang; (22):1−56, 2023.
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Bayesian Data Selection: Eli N. Weinstein, Jeffrey W. Miller; (23):1−72, 2023.
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Calibrated Multiple-Output Quantile Regression with Representation Learning: Shai Feldman, Stephen Bates, Yaniv Romano; (24):1−48, 2023.
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Discrete Variational Calculus for Accelerated Optimization: Cédric M. Campos, Alejandro Mahillo, David Martín de Diego; (25):1−33, 2023.
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Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels: Hao Wang, Rui Gao, Flavio P. Calmon; (26):1−43, 2023.
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The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time: Raj Agrawal, Tamara Broderick; (27):1−60, 2023.
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Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping: XuranMeng, JeffYao; (28):1−40, 2023. (Machine Learning Open Source Software Paper)
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HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn: Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard; (29):1−17, 2023. (Machine Learning Open Source Software Paper)
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Attacks against Federated Learning Defense Systems and their Mitigation: Cody Lewis, Vijay Varadharajan, Nasimul Noman; (30):1−50, 2023.
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Labels, Information, and Computation: Efficient Learning Using Sufficient Labels: Shiyu Duan, Spencer Chang, Jose C. Principe; (31):1−35, 2023.
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Sparse PCA: a Geometric Approach: Dimitris Bertsimas, Driss Lahlou Kitane; (32):1−33, 2023.
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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; (33):1−57, 2023.
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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond: Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne; (34):1−11, 2023. (Machine Learning Open Source Software Paper)
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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; (35):1−52, 2023.
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Label Distribution Changing Learning with Sample Space Expanding: Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou; (36):1−48, 2023.
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Ridges, Neural Networks, and the Radon Transform: Michael Unser; (37):1−33, 2023.
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First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems: Michael I. Jordan, Tianyi Lin, Manolis Zampetakis; (38):1−46, 2023.
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Sensing Theorems for Unsupervised Learning in Linear Inverse Problems: Julián Tachella, Dongdong Chen, Mike Davies; (39):1−45, 2023.
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On Batch Teaching Without Collusion: Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles; (40):1−33, 2023.
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Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data: Shaowu Pan, Steven L. Brunton, J. Nathan Kutz; (41):1−60, 2023.
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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; (42):1−63, 2023.
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Benchmarking Graph Neural Networks: Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson; (43):1−48, 2023.
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Robust Load Balancing with Machine Learned Advice: Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng; (44):1−46, 2023.
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The multimarginal optimal transport formulation of adversarial multiclass classification: Nicolás García Trillos, Matt Jacobs, Jakwang Kim; (45):1−56, 2023.
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The d-Separation Criterion in Categorical Probability: Tobias Fritz, Andreas Klingler; (46):1−49, 2023.
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A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering: Haizi Yu, Igor Mineyev, Lav R. Varshney; (47):1−61, 2023.
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On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size: Xiaoyu Wang, Ya-xiang Yuan; (48):1−49, 2023.
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Reinforcement Learning for Joint Optimization of Multiple Rewards: Mridul Agarwal, Vaneet Aggarwal; (49):1−41, 2023.
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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; (50):1−64, 2023.
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Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks: Lingjun Li, Jun Li; (51):1−44, 2023.
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Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems: Kunal Pattanayak, Vikram Krishnamurthy; (52):1−64, 2023.
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VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback: Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I Jordan, Ion Stoica; (53):1−45, 2023.
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Contextual Stochastic Block Model: Sharp Thresholds and Contiguity: Chen Lu, Subhabrata Sen; (54):1−34, 2023.
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Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches: Shaogao Lv, Xin He, Junhui Wang; (55):1−38, 2023.
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On the geometry of Stein variational gradient descent: Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch; (56):1−39, 2023.
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Tree-AMP: Compositional Inference with Tree Approximate Message Passing: Antoine Baker, Florent Krzakala, Benjamin Aubin, Lenka Zdeborová; (57):1−89, 2023.
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Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval: Yan Shuo Tan, Roman Vershynin; (58):1−47, 2023.
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Topological Convolutional Layers for Deep Learning: Ephy R. Love, Benjamin Filippenko, Vasileios Maroulas, Gunnar Carlsson; (59):1−35, 2023.
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Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints: Qinbo Bai, Vaneet Aggarwal, Ather Gattami; (60):1−25, 2023.
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Density estimation on low-dimensional manifolds: an inflation-deflation approach: Christian Horvat, Jean-Pascal Pfister; (61):1−37, 2023.
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Monotonic Alpha-divergence Minimisation for Variational Inference: Kamélia Daudel, Randal Douc, François Roueff; (62):1−76, 2023.
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On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results: Marcelo Arenas, Pablo Barcelo, Leopoldo Bertossi, Mikael Monet; (63):1−58, 2023.
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Fundamental limits and algorithms for sparse linear regression with sublinear sparsity: Lan V. Truong; (64):1−49, 2023.
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Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule: Nikhil Iyer, V. Thejas, Nipun Kwatra, Ramachandran Ramjee, Muthian Sivathanu; (65):1−37, 2023.
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Posterior Contraction for Deep Gaussian Process Priors: Gianluca Finocchio, Johannes Schmidt-Hieber; (66):1−49, 2023.
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Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching: Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann, Arto Klami; (67):1−51, 2023.
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Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data: Ruoyu Wang, Miaomiao Su, Qihua Wang; (68):1−52, 2023.
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When Locally Linear Embedding Hits Boundary: Hau-Tieng Wu, Nan Wu; (69):1−80, 2023.
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Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection: Jonathan Hillman, Toby Dylan Hocking; (70):1−24, 2023.
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Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition: Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau; (71):1−57, 2023.
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How Do You Want Your Greedy: Simultaneous or Repeated?: Moran Feldman, Christopher Harshaw, Amin Karbasi; (72):1−87, 2023.
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Inference for a Large Directed Acyclic Graph with Unspecified Interventions: Chunlin Li, Xiaotong Shen, Wei Pan; (73):1−48, 2023.
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Privacy-Aware Rejection Sampling: Jordan Awan, Vinayak Rao; (74):1−32, 2023.
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Intrinsic Persistent Homology via Density-based Metric Learning: Ximena Fernández, Eugenio Borghini, Gabriel Mindlin, Pablo Groisman; (75):1−42, 2023.
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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; (76):1−31, 2023.
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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; (77):1−42, 2023.
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Towards Learning to Imitate from a Single Video Demonstration: Glen Berseth, Florian Golemo, Christopher Pal; (78):1−26, 2023.
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Approximate Post-Selective Inference for Regression with the Group LASSO: Snigdha Panigrahi, Peter W MacDonald, Daniel Kessler; (79):1−49, 2023.
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Temporal Abstraction in Reinforcement Learning with the Successor Representation: Marlos C. Machado, Andre Barreto, Doina Precup, Michael Bowling; (80):1−69, 2023.
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Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics: Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill; (81):1−36, 2023.
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Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality: Ning Ning, Edward L. Ionides; (82):1−76, 2023.
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Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees: William J. Wilkinson, Simo Särkkä, Arno Solin; (83):1−50, 2023.
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Online Optimization over Riemannian Manifolds: Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi; (84):1−67, 2023.
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Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence: Henry Lam, Haofeng Zhang; (85):1−58, 2023.
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Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces: George Stepaniants; (86):1−72, 2023.
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Gaussian Processes with Errors in Variables: Theory and Computation: Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll; (87):1−53, 2023.
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Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data: Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson; (88):1−49, 2023.
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Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs: Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar; (89):1−97, 2023.
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Outlier-Robust Subsampling Techniques for Persistent Homology: Bernadette J. Stolz; (90):1−35, 2023.
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Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds: Likai Chen, Georg Keilbar, Wei Biao Wu; (91):1−25, 2023.
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Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption: Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang; (92):1−46, 2023.
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Decentralized Learning: Theoretical Optimality and Practical Improvements: Yucheng Lu, Christopher De Sa; (93):1−62, 2023.
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Faith-Shap: The Faithful Shapley Interaction Index: Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar; (94):1−42, 2023.
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Statistical Inference for Noisy Incomplete Binary Matrix: Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu; (95):1−66, 2023.
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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; (96):1−84, 2023.
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Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation: Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu; (97):1−30, 2023.
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An Analysis of Robustness of Non-Lipschitz Networks: Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang; (98):1−43, 2023.
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Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity: Artem Vysogorets, Julia Kempe; (99):1−23, 2023.
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FedLab: A Flexible Federated Learning Framework: Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu; (100):1−7, 2023. (Machine Learning Open Source Software Paper)
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Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds: Didong Li, Wenpin Tang, Sudipto Banerjee; (101):1−26, 2023.
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Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments: Haixu Ma, Donglin Zeng, Yufeng Liu; (102):1−48, 2023.
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Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint: Michael R. Metel; (103):1−44, 2023.
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Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics: Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang; (104):1−42, 2023.
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Knowledge Hypergraph Embedding Meets Relational Algebra: Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole; (105):1−34, 2023.
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Concentration analysis of multivariate elliptic diffusions: Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch; (106):1−38, 2023.
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Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption: Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile; (107):1−31, 2023.
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Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors: Michail Spitieris, Ingelin Steinsland; (108):1−39, 2023.
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Dimensionless machine learning: Imposing exact units equivariance: Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu; (109):1−32, 2023.
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A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates: Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi; (110):1−43, 2023.
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FLIP: A Utility Preserving Privacy Mechanism for Time Series: Tucker McElroy, Anindya Roy, Gaurab Hore; (111):1−29, 2023.
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The Hyperspherical Geometry of Community Detection: Modularity as a Distance: Martijn Gösgens, Remco van der Hofstad, Nelly Litvak; (112):1−36, 2023.
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The Implicit Bias of Benign Overfitting: Ohad Shamir; (113):1−40, 2023.
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Generalization Bounds for Adversarial Contrastive Learning: Xin Zou, Weiwei Liu; (114):1−54, 2023.
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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; (115):1−53, 2023.
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SQLFlow: An Extensible Toolkit Integrating DB and AI: Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen; (116):1−9, 2023. (Machine Learning Open Source Software Paper)
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Deep linear networks can benignly overfit when shallow ones do: Niladri S. Chatterji, Philip M. Long; (117):1−39, 2023.
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A Unified Framework for Optimization-Based Graph Coarsening: Manoj Kumar, Anurag Sharma, Sandeep Kumar; (118):1−50, 2023.
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An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks: Stefan Stein, Chenlei Leng; (119):1−69, 2023.
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Maximum likelihood estimation in Gaussian process regression is ill-posed: Toni Karvonen, Chris J. Oates; (120):1−47, 2023.
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Minimal Width for Universal Property of Deep RNN: Chang hoon Song, Geonho Hwang, Jun ho Lee, Myungjoo Kang; (121):1−41, 2023.
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Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities: Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock; (122):1−77, 2023.
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Benign overfitting in ridge regression: Alexander Tsigler, Peter L. Bartlett; (123):1−76, 2023.
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HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation: Weijie J. Su, Yuancheng Zhu; (124):1−53, 2023.
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Statistical Robustness of Empirical Risks in Machine Learning: Shaoyan Guo, Huifu Xu, Liwei Zhang; (125):1−38, 2023.
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Euler-Lagrange Analysis of Generative Adversarial Networks: Siddarth Asokan, Chandra Sekhar Seelamantula; (126):1−100, 2023.
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Graph Clustering with Graph Neural Networks: Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller; (127):1−21, 2023.
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An Eigenmodel for Dynamic Multilayer Networks: Joshua Daniel Loyal, Yuguo Chen; (128):1−69, 2023.
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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; (129):1−23, 2023.
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Combinatorial Optimization and Reasoning with Graph Neural Networks: Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic; (130):1−61, 2023.
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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; (131):1−44, 2023.
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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; (132):1−57, 2023.
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Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators: Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill; (133):1−57, 2023.
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MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation: Qian Li, Binyan Jiang, Defeng Sun; (134):1−44, 2023.
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Sparse GCA and Thresholded Gradient Descent: Sheng Gao, Zongming Ma; (135):1−61, 2023.
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Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection: Wenhao Li, Ningyuan Chen, L. Jeff Hong; (136):1−84, 2023.
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Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks: Hui Jin, Guido Montufar; (137):1−97, 2023.
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Asymptotics of Network Embeddings Learned via Subsampling: Andrew Davison, Morgane Austern; (138):1−120, 2023.
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Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games: Ben Hambly, Renyuan Xu, Huining Yang; (139):1−56, 2023.
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Jump Interval-Learning for Individualized Decision Making with Continuous Treatments: Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu; (140):1−92, 2023.
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Optimal Convergence Rates for Distributed Nystroem Approximation: Jian Li, Yong Liu, Weiping Wang; (141):1−39, 2023.
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On Tilted Losses in Machine Learning: Theory and Applications: Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith; (142):1−79, 2023.
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Large sample spectral analysis of graph-based multi-manifold clustering: Nicolas Garcia Trillos, Pengfei He, Chenghui Li; (143):1−71, 2023.
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Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering: Noirrit Kiran Chandra, Antonio Canale, David B. Dunson; (144):1−42, 2023.
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Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning: Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang; (145):1−46, 2023.
[abs][pdf][bib] [code]

Off-Policy Actor-Critic with Emphatic Weightings: Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White; (146):1−63, 2023.
[abs][pdf][bib] [code]

Stochastic Optimization under Distributional Drift: Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui; (147):1−56, 2023.
[abs][pdf][bib]

Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition: Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang; (148):1−63, 2023.
[abs][pdf][bib]

Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning: Titouan Vayer, Rémi Gribonval; (149):1−51, 2023.
[abs][pdf][bib]

MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning: Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang; (150):1−12, 2023. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Generalization error bounds for multiclass sparse linear classifiers: Tomer Levy, Felix Abramovich; (151):1−35, 2023.
[abs][pdf][bib]

Selective inference for k-means clustering: Yiqun T. Chen, Daniela M. Witten; (152):1−41, 2023.
[abs][pdf][bib] [code]

Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process: Cheng Zeng, Jeffrey W Miller, Leo L Duan; (153):1−32, 2023.
[abs][pdf][bib] [code]

Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees: Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd; (154):1−48, 2023.
[abs][pdf][bib] [code]

Adaptive Data Depth via Multi-Armed Bandits: Tavor Baharav, Tze Leung Lai; (155):1−29, 2023.
[abs][pdf][bib]

Integrating Random Effects in Deep Neural Networks: Giora Simchoni, Saharon Rosset; (156):1−57, 2023.
[abs][pdf][bib] [code]

Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity: Huan Li, Zhouchen Lin; (157):1−37, 2023.
[abs][pdf][bib] [code]

Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees: Hamid Reza Feyzmahdavian, Mikael Johansson; (158):1−75, 2023.
[abs][pdf][bib]

Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations: Arnab Ganguly, Riten Mitra, Jinpu Zhou; (159):1−39, 2023.
[abs][pdf][bib]

Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling: Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron; (160):1−65, 2023.
[abs][pdf][bib] [code]

q-Learning in Continuous Time: Yanwei Jia, Xun Yu Zhou; (161):1−61, 2023.
[abs][pdf][bib] [code]

Flexible Model Aggregation for Quantile Regression: Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani; (162):1−45, 2023.
[abs][pdf][bib] [code]

Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification: Gavin Zhang, Salar Fattahi, Richard Y. Zhang; (163):1−55, 2023.
[abs][pdf][bib]

A Framework and Benchmark for Deep Batch Active Learning for Regression: David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart; (164):1−81, 2023.
[abs][pdf][bib] [code]

Robust Methods for High-Dimensional Linear Learning: Ibrahim Merad, Stéphane Gaïffas; (165):1−44, 2023.
[abs][pdf][bib]

A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition: Masaru Ito, Zhaosong Lu, Chuan He; (166):1−34, 2023.
[abs][pdf][bib]

Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start: Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo; (167):1−37, 2023.
[abs][pdf][bib] [code]

Inference on the Change Point under a High Dimensional Covariance Shift: Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis; (168):1−68, 2023.
[abs][pdf][bib]

DART: Distance Assisted Recursive Testing: Xuechan Li, Anthony D. Sung, Jichun Xie; (169):1−41, 2023.
[abs][pdf][bib]

Small Transformers Compute Universal Metric Embeddings: Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić; (170):1−48, 2023.
[abs][pdf][bib] [code]

Incremental Learning in Diagonal Linear Networks: Raphaël Berthier; (171):1−26, 2023.
[abs][pdf][bib]

Beyond the Golden Ratio for Variational Inequality Algorithms: Ahmet Alacaoglu, Axel Böhm, Yura Malitsky; (172):1−33, 2023.
[abs][pdf][bib] [code]

From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions: Johannes Resin; (173):1−21, 2023.
[abs][pdf][bib]

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