JMLR Volume 23

Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models: Subhabrata Majumdar, George Michailidis; (1):1−53, 2022.
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Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions: Shaogao Lv, Heng Lian; (2):1−32, 2022.
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Recovering shared structure from multiple networks with unknown edge distributions: Keith Levin, Asad Lodhia, Elizaveta Levina; (3):1−48, 2022.
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Exploiting locality in high-dimensional Factorial hidden Markov models: Lorenzo Rimella, Nick Whiteley; (4):1−34, 2022.
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Empirical Risk Minimization under Random Censorship: Guillaume Ausset, Stephan Clémençon, François Portier; (5):1−59, 2022.
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XAI Beyond Classification: Interpretable Neural Clustering: Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou; (6):1−28, 2022.
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Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes: Justin D. Silverman, Kimberly Roche, Zachary C. Holmes, Lawrence A. David, Sayan Mukherjee; (7):1−42, 2022.
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Deep Learning in Target Space: Michael Fairbank, Spyridon Samothrakis, Luca Citi; (8):1−46, 2022.
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Scaling Laws from the Data Manifold Dimension: Utkarsh Sharma, Jared Kaplan; (9):1−34, 2022.
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Interpolating Predictors in High-Dimensional Factor Regression: Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp; (10):1−60, 2022.
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Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes: Ali Kara, Serdar Yuksel; (11):1−46, 2022.
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Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems: Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan; (12):1−83, 2022.
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Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality: Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet; (13):1−35, 2022.
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On Generalizations of Some Distance Based Classifiers for HDLSS Data: Sarbojit Roy, Soham Sarkar, Subhajit Dutta, Anil K. Ghosh; (14):1−41, 2022.
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A Stochastic Bundle Method for Interpolation: Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar; (15):1−57, 2022.
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TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems: Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Schönlieb; (16):1−48, 2022.
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Spatial Multivariate Trees for Big Data Bayesian Regression: Michele Peruzzi, David B. Dunson; (17):1−40, 2022.
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Decimated Framelet System on Graphs and Fast G-Framelet Transforms: Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang; (18):1−68, 2022.
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Universal Approximation in Dropout Neural Networks: Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies, Jaron Sanders, Albert Senen-Cerda; (19):1−46, 2022.
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Supervised Dimensionality Reduction and Visualization using Centroid-Encoder: Tomojit Ghosh, Michael Kirby; (20):1−34, 2022.
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Evolutionary Variational Optimization of Generative Models: Jakob Drefs, Enrico Guiraud, Jörg Lücke; (21):1−51, 2022.
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LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data: Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney; (22):1−36, 2022.
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Fast and Robust Rank Aggregation against Model Misspecification: Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama; (23):1−35, 2022.
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On Biased Stochastic Gradient Estimation: Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb; (24):1−43, 2022.
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Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting: Maxime Vono, Daniel Paulin, Arnaud Doucet; (25):1−69, 2022.
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MurTree: Optimal Decision Trees via Dynamic Programming and Search: Emir Demirović, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Peter J. Stuckey; (26):1−47, 2022.
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Data-Derived Weak Universal Consistency: Narayana Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski; (27):1−55, 2022.
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Novel Min-Max Reformulations of Linear Inverse Problems: Mohammed Rayyan Sheriff, Debasish Chatterjee; (28):1−46, 2022.
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Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning: Kaiyi Ji, Junjie Yang, Yingbin Liang; (29):1−41, 2022.
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A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One: Augusto Fasano, Daniele Durante; (30):1−26, 2022.
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An Improper Estimator with Optimal Excess Risk in Misspecified Density Estimation and Logistic Regression: Jaouad Mourtada, Stéphane Gaïffas; (31):1−49, 2022.
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Active Learning for Nonlinear System Identification with Guarantees: Horia Mania, Michael I. Jordan, Benjamin Recht; (32):1−30, 2022.
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Model Averaging Is Asymptotically Better Than Model Selection For Prediction: Tri M. Le, Bertrand S. Clarke; (33):1−53, 2022.
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SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks: Weijing Tang, Jiaqi Ma, Qiaozhu Mei, Ji Zhu; (34):1−29, 2022.
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Optimality and Stability in Non-Convex Smooth Games: Guojun Zhang, Pascal Poupart, Yaoliang Yu; (35):1−71, 2022.
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Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization: Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang; (36):1−70, 2022.
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Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric: Matteo Pegoraro, Mario Beraha; (37):1−59, 2022.
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Score Matched Neural Exponential Families for Likelihood-Free Inference: Lorenzo Pacchiardi, Ritabrata Dutta; (38):1−71, 2022.
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(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics: Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet; (39):1−70, 2022.
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Structure-adaptive Manifold Estimation: Nikita Puchkin, Vladimir Spokoiny; (40):1−62, 2022.
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The Correlation-assisted Missing Data Estimator: Timothy I. Cannings, Yingying Fan; (41):1−49, 2022.
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Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks: Zhong Li, Jiequn Han, Weinan E, Qianxiao Li; (42):1−85, 2022.
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Sampling Permutations for Shapley Value Estimation: Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes; (43):1−46, 2022.
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PAC Guarantees and Effective Algorithms for Detecting Novel Categories: Si Liu, Risheek Garrepalli, Dan Hendrycks, Alan Fern, Debashis Mondal, Thomas G. Dietterich; (44):1−47, 2022.
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Optimal Transport for Stationary Markov Chains via Policy Iteration: Kevin O'Connor, Kevin McGoff, Andrew B. Nobel; (45):1−52, 2022.
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Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent: Wanrong Zhu, Zhipeng Lou, Wei Biao Wu; (46):1−22, 2022.
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Cascaded Diffusion Models for High Fidelity Image Generation: Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans; (47):1−33, 2022.
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Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis: Zhiyan Ding, Shi Chen, Qin Li, Stephen J. Wright; (48):1−65, 2022.
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Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection: Xinyi Wang, Lang Tong; (49):1−27, 2022.
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Analytically Tractable Hidden-States Inference in Bayesian Neural Networks: Luong-Ha Nguyen, James-A. Goulet; (50):1−33, 2022.
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Toolbox for Multimodal Learn (scikit-multimodallearn): Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette; (51):1−7, 2022. (Machine Learning Open Source Software Paper)
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LinCDE: Conditional Density Estimation via Lindsey's Method: Zijun Gao, Trevor Hastie; (52):1−55, 2022.
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DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python: Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler; (53):1−6, 2022. (Machine Learning Open Source Software Paper)
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SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization: Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter; (54):1−9, 2022. (Machine Learning Open Source Software Paper)
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Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy: Terrance D. Savitsky, Matthew R.Williams, Jingchen Hu; (55):1−37, 2022.
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solo-learn: A Library of Self-supervised Methods for Visual Representation Learning: Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci; (56):1−6, 2022. (Machine Learning Open Source Software Paper)
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Inherent Tradeoffs in Learning Fair Representations: Han Zhao, Geoffrey J. Gordon; (57):1−26, 2022.
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A Statistical Approach for Optimal Topic Model Identification: Craig M. Lewis, Francesco Grossetti; (58):1−20, 2022.
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Causal Classification: Treatment Effect Estimation vs. Outcome Prediction: Carlos Fernández-Loría, Foster Provost; (59):1−35, 2022.
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A Unifying Framework for Variance-Reduced Algorithms for Findings Zeroes of Monotone operators: Xun Zhang, William B. Haskell, Zhisheng Ye; (60):1−44, 2022.
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Sparse Additive Gaussian Process Regression: Hengrui Luo, Giovanni Nattino, Matthew T. Pratola; (61):1−34, 2022.
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The AIM and EM Algorithms for Learning from Coarse Data: Manfred Jaeger; (62):1−55, 2022.
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Additive Nonlinear Quantile Regression in Ultra-high Dimension: Ben Sherwood, Adam Maidman; (63):1−47, 2022.
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Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity: Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra; (64):1−47, 2022.
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On the Complexity of Approximating Multimarginal Optimal Transport: Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan; (65):1−43, 2022.
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New Insights for the Multivariate Square-Root Lasso: Aaron J. Molstad; (66):1−52, 2022.
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Are All Layers Created Equal?: Chiyuan Zhang, Samy Bengio, Yoram Singer; (67):1−28, 2022.
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Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters: Wei Zhu, Qiang Qiu, Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng; (68):1−45, 2022.
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Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method: Alex Olshevsky; (69):1−32, 2022.
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Generalized Sparse Additive Models: Asad Haris, Noah Simon, Ali Shojaie; (70):1−56, 2022.
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Multiple-Splitting Projection Test for High-Dimensional Mean Vectors: Wanjun Liu, Xiufan Yu, Runze Li; (71):1−27, 2022.
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Batch Normalization Preconditioning for Neural Network Training: Susanna Lange, Kyle Helfrich, Qiang Ye; (72):1−41, 2022.
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A Kernel Two-Sample Test for Functional Data: George Wynne, Andrew B. Duncan; (73):1−51, 2022.
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All You Need is a Good Functional Prior for Bayesian Deep Learning: Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone; (74):1−56, 2022.
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Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling: Gábor Melis, András György, Phil Blunsom; (75):1−36, 2022.
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Joint Inference of Multiple Graphs from Matrix Polynomials: Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra; (76):1−35, 2022.
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Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits: Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec; (77):1−40, 2022.
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Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism: Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos; (78):1−49, 2022.
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Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors: Yuling Yao, Aki Vehtari, Andrew Gelman; (79):1−45, 2022.
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Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures: Marta Catalano, Pierpaolo De Blasi, Antonio Lijoi, Igor Pruenster; (80):1−23, 2022.
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Dependent randomized rounding for clustering and partition systems with knapsack constraints: David G. Harris, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh; (81):1−41, 2022.
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FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting: Boxin Zhao, Y. Samuel Wang, Mladen Kolar; (82):1−82, 2022.
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Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping: Yichi Zhang, Molei Liu, Matey Neykov, Tianxi Cai; (83):1−25, 2022.
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Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior: Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky, Sanvesh Srivastava; (84):1−59, 2022.
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A Distribution Free Conditional Independence Test with Applications to Causal Discovery: Zhanrui Cai, Runze Li, Yaowu Zhang; (85):1−41, 2022.
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Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing: Chao Shen, Yu-Ting Lin, Hau-Tieng Wu; (86):1−30, 2022.
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CD-split and HPD-split: Efficient Conformal Regions in High Dimensions: Rafael Izbicki, Gilson Shimizu, Rafael B. Stern; (87):1−32, 2022.
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Generalized Ambiguity Decomposition for Ranking Ensemble Learning: Hongzhi Liu, Yingpeng Du, Zhonghai Wu; (88):1−36, 2022.
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Machine Learning on Graphs: A Model and Comprehensive Taxonomy: Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy; (89):1−64, 2022.
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Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling: Xi Chen, Bo Jiang, Tianyi Lin, Shuzhong Zhang; (90):1−38, 2022.
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When Hardness of Approximation Meets Hardness of Learning: Eran Malach, Shai Shalev-Shwartz; (91):1−24, 2022.
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Gauss-Legendre Features for Gaussian Process Regression: Paz Fink Shustin, Haim Avron; (92):1−47, 2022.
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Regularized K-means Through Hard-Thresholding: Jakob Raymaekers, Ruben H. Zamar; (93):1−48, 2022.
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Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach: Kweku Abraham, Ismaël Castillo, Elisabeth Gassiat; (94):1−57, 2022.
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Attraction-Repulsion Spectrum in Neighbor Embeddings: Jan Niklas Böhm, Philipp Berens, Dmitry Kobak; (95):1−32, 2022.
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Rethinking Nonlinear Instrumental Variable Models through Prediction Validity: Chunxiao Li, Cynthia Rudin, Tyler H. McCormick; (96):1−55, 2022.
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Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective: Daniel Sanz-Alonso, Ruiyi Yang; (97):1−28, 2022.
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PECOS: Prediction for Enormous and Correlated Output Spaces: Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon; (98):1−32, 2022.
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Distributed Learning of Finite Gaussian Mixtures: Qiong Zhang, Jiahua Chen; (99):1−40, 2022.
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Total Stability of SVMs and Localized SVMs: Hannes Köhler, Andreas Christmann; (100):1−41, 2022.
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Towards An Efficient Approach for the Nonconvex lp Ball Projection: Algorithm and Analysis: Xiangyu Yang, Jiashan Wang, Hao Wang; (101):1−31, 2022.
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Sufficient reductions in regression with mixed predictors: Efstathia Bura, Liliana Forzani, Rodrigo Garcia Arancibia, Pamela Llop, Diego Tomassi; (102):1−47, 2022.
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The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures: Nir Weinberger, Guy Bresler; (103):1−79, 2022.
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Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency: F. Richard Guo, Emilija Perković; (104):1−41, 2022.
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Globally Injective ReLU Networks: Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanić, Maarten de Hoop; (105):1−55, 2022.
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Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold: Bokun Wang, Shiqian Ma, Lingzhou Xue; (106):1−33, 2022.
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IALE: Imitating Active Learner Ensembles: Christoffer Löffler, Christopher Mutschler; (107):1−29, 2022.
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Bayesian subset selection and variable importance for interpretable prediction and classification: Daniel R. Kowal; (108):1−38, 2022.
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Conditions and Assumptions for Constraint-based Causal Structure Learning: Kayvan Sadeghi, Terry Soo; (109):1−34, 2022.
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EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation: Jun Ho Yoon, Seyoung Kim; (110):1−39, 2022.
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Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces: Masaaki Imaizumi, Kenji Fukumizu; (111):1−54, 2022.
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Sum of Ranked Range Loss for Supervised Learning: Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu; (112):1−44, 2022.
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The Two-Sided Game of Googol: José Correa, Andrés Cristi, Boris Epstein, José Soto; (113):1−37, 2022.
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ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction: Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma; (114):1−103, 2022.
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Cauchy–Schwarz Regularized Autoencoder: Linh Tran, Maja Pantic, Marc Peter Deisenroth; (115):1−37, 2022.
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An Error Analysis of Generative Adversarial Networks for Learning Distributions: Jian Huang, Yuling Jiao, Zhen Li, Shiao Liu, Yang Wang, Yunfei Yang; (116):1−43, 2022.
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OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems: Chelsea Sidrane, Amir Maleki, Ahmed Irfan, Mykel J. Kochenderfer; (117):1−45, 2022.
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Under-bagging Nearest Neighbors for Imbalanced Classification: Hanyuan Hang, Yuchao Cai, Hanfang Yang, Zhouchen Lin; (118):1−63, 2022.
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A spectral-based analysis of the separation between two-layer neural networks and linear methods: Lei Wu, Jihao Long; (119):1−34, 2022.
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Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity: William Fedus, Barret Zoph, Noam Shazeer; (120):1−39, 2022.
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Online Mirror Descent and Dual Averaging: Keeping Pace in the Dynamic Case: Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander; (121):1−38, 2022.
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Depth separation beyond radial functions: Luca Venturi, Samy Jelassi, Tristan Ozuch, Joan Bruna; (122):1−56, 2022.
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Provable Tensor-Train Format Tensor Completion by Riemannian Optimization: Jian-Feng Cai, Jingyang Li, Dong Xia; (123):1−77, 2022.
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Darts: User-Friendly Modern Machine Learning for Time Series: Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, Maxime Dumonal, Jan Kościsz, Dennis Bader, Frédérick Gusset, Mounir Benheddi, Camila Williamson, Michal Kosinski, Matej Petrik, Gaël Grosch; (124):1−6, 2022. (Machine Learning Open Source Software Paper)
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Foolish Crowds Support Benign Overfitting: Niladri S. Chatterji, Philip M. Long; (125):1−12, 2022.
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Neural Estimation of Statistical Divergences: Sreejith Sreekumar, Ziv Goldfeld; (126):1−75, 2022.
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Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations: Haoyuan Chen, Liang Ding, Rui Tuo; (127):1−32, 2022.
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Power Iteration for Tensor PCA: Jiaoyang Huang, Daniel Z. Huang, Qing Yang, Guang Cheng; (128):1−47, 2022.
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On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC): Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, Satish V. Ukkusuri; (129):1−46, 2022.
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Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks: Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli; (130):1−55, 2022.
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Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence: Julie Nutini, Issam Laradji, Mark Schmidt; (131):1−74, 2022.
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An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference: Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas; (132):1−109, 2022.
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Manifold Coordinates with Physical Meaning: Samson J. Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen; (133):1−57, 2022.
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Transfer Learning in Information Criteria-based Feature Selection: Shaohan Chen, Nikolaos V. Sahinidis, Chuanhou Gao; (134):1−105, 2022.
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Recovery and Generalization in Over-Realized Dictionary Learning: Jeremias Sulam, Chong You, Zhihui Zhu; (135):1−23, 2022.
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Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization: Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok; (136):1−60, 2022.
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On the Efficiency of Entropic Regularized Algorithms for Optimal Transport: Tianyi Lin, Nhat Ho, Michael I. Jordan; (137):1−42, 2022.
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Exact simulation of diffusion first exit times: algorithm acceleration: Samuel Herrmann, Cristina Zucca; (138):1−20, 2022.
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No Weighted-Regret Learning in Adversarial Bandits with Delays: Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet; (139):1−43, 2022.
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Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems: Yahya Sattar, Samet Oymak; (140):1−49, 2022.
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The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks: Konstantinos Pantazis, Avanti Athreya, Jesus Arroyo, William N Frost, Evan S Hill, Vince Lyzinski; (141):1−77, 2022.
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A Perturbation-Based Kernel Approximation Framework: Roy Mitz, Yoel Shkolnisky; (142):1−26, 2022.
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Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning: Alex A. Gorodetsky, Cosmin Safta, John D. Jakeman; (143):1−29, 2022.
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A Momentumized, Adaptive, Dual Averaged Gradient Method: Aaron Defazio, Samy Jelassi; (144):1−34, 2022.
[abs][pdf][bib] [code]

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning: Andrew Patterson, Adam White, Martha White; (145):1−61, 2022.
[abs][pdf][bib] [code]

Adversarial Robustness Guarantees for Gaussian Processes: Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts, Marta Kwiatkowska; (146):1−55, 2022.
[abs][pdf][bib] [code]

On the Robustness to Misspecification of α-posteriors and Their Variational Approximations: Marco Avella Medina, José Luis Montiel Olea, Cynthia Rush, Amilcar Velez; (147):1−51, 2022.
[abs][pdf][bib]

Online Nonnegative CP-dictionary Learning for Markovian Data: Hanbaek Lyu, Christopher Strohmeier, Deanna Needell; (148):1−50, 2022.
[abs][pdf][bib] [code]

Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning: Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon; (149):1−43, 2022.
[abs][pdf][bib] [code]

EV-GAN: Simulation of extreme events with ReLU neural networks: Michaël Allouche, Stéphane Girard, Emmanuel Gobet; (150):1−39, 2022.
[abs][pdf][bib]

Universal Approximation of Functions on Sets: Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Michael A. Osborne, Ingmar Posner; (151):1−56, 2022.
[abs][pdf][bib]

Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning: Sébastien Forestier, Rémy Portelas, Yoan Mollard, Pierre-Yves Oudeyer; (152):1−41, 2022.
[abs][pdf][bib]

Truncated Emphatic Temporal Difference Methods for Prediction and Control: Shangtong Zhang, Shimon Whiteson; (153):1−59, 2022.
[abs][pdf][bib] [code]

Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach: Yanwei Jia, Xun Yu Zhou; (154):1−55, 2022.
[abs][pdf][bib] [code]

Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks: Guy Hacohen, Daphna Weinshall; (155):1−46, 2022.
[abs][pdf][bib]

Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification: Yingying Zhang, Yan-Yong Zhao, Heng Lian; (156):1−24, 2022.
[abs][pdf][bib]

A universally consistent learning rule with a universally monotone error: Vladimir Pestov; (157):1−27, 2022.
[abs][pdf][bib]

ktrain: A Low-Code Library for Augmented Machine Learning: Arun S. Maiya; (158):1−6, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Structure Learning for Directed Trees: Martin E. Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters; (159):1−97, 2022.
[abs][pdf][bib] [code]

Fairness-Aware PAC Learning from Corrupted Data: Nikola Konstantinov, Christoph H. Lampert; (160):1−60, 2022.
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Topologically penalized regression on manifolds: Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Clément Levrard, Wolfgang Polonik; (161):1−39, 2022.
[abs][pdf][bib] [code]

Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods: Dachao Lin, Haishan Ye, Zhihua Zhang; (162):1−40, 2022.
[abs][pdf][bib]

Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements: Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin Tripp, Yuejie Chi; (163):1−77, 2022.
[abs][pdf][bib] [code]

Solving L1-regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation: Antoine Dedieu, Rahul Mazumder, Haoyue Wang; (164):1−41, 2022.
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Improved Classification Rates for Localized SVMs: Ingrid Blaschzyk, Ingo Steinwart; (165):1−59, 2022.
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Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects: Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag; (166):1−50, 2022.
[abs][pdf][bib]

Unbiased estimators for random design regression: Michał Dereziński, Manfred K. Warmuth, Daniel Hsu; (167):1−46, 2022.
[abs][pdf][bib]

A Worst Case Analysis of Calibrated Label Ranking Multi-label Classification Method: Lucas Henrique Sousa Mello, Flávio Miguel Varejão, Alexandre Loureiros Rodrigues; (168):1−30, 2022.
[abs][pdf][bib]

D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data: Hai Shu, Zhe Qu, Hongtu Zhu; (169):1−64, 2022.
[abs][pdf][bib] [code]

Scalable and Efficient Hypothesis Testing with Random Forests: Tim Coleman, Wei Peng, Lucas Mentch; (170):1−35, 2022.
[abs][pdf][bib]

Interlocking Backpropagation: Improving depthwise model-parallelism: Aidan N. Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal; (171):1−28, 2022.
[abs][pdf][bib]

Projection-free Distributed Online Learning with Sublinear Communication Complexity: Yuanyu Wan, Guanghui Wang, Wei-Wei Tu, Lijun Zhang; (172):1−53, 2022.
[abs][pdf][bib]

Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training: Diego Granziol, Stefan Zohren, Stephen Roberts; (173):1−65, 2022.
[abs][pdf][bib]

Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models: Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman; (174):1−37, 2022.
[abs][pdf][bib]

Improved Generalization Bounds for Adversarially Robust Learning: Idan Attias, Aryeh Kontorovich, Yishay Mansour; (175):1−31, 2022.
[abs][pdf][bib]

Signature Moments to Characterize Laws of Stochastic Processes: Ilya Chevyrev, Harald Oberhauser; (176):1−42, 2022.
[abs][pdf][bib]

Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms: Ping Ma, Yongkai Chen, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney; (177):1−45, 2022.
[abs][pdf][bib]

Logarithmic Regret for Episodic Continuous-Time Linear-Quadratic Reinforcement Learning over a Finite-Time Horizon: Matteo Basei, Xin Guo, Anran Hu, Yufei Zhang; (178):1−34, 2022.
[abs][pdf][bib]

KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints: Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz; (179):1−66, 2022.
[abs][pdf][bib]

Matrix Completion with Covariate Information and Informative Missingness: Huaqing Jin, Yanyuan Ma, Fei Jiang; (180):1−62, 2022.
[abs][pdf][bib] [code]

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent: David Holzmüller, Ingo Steinwart; (181):1−82, 2022.
[abs][pdf][bib] [code]

Extensions to the Proximal Distance Method of Constrained Optimization: Alfonso Landeros, Oscar Hernan Madrid Padilla, Hua Zhou, Kenneth Lange; (182):1−45, 2022.
[abs][pdf][bib] [code]

Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution: Yichen Zhou, Giles Hooker; (183):1−44, 2022.
[abs][pdf][bib] [code]

Statistical Optimality and Stability of Tangent Transform Algorithms in Logit Models: Indrajit Ghosh, Anirban Bhattacharya, Debdeep Pati; (184):1−42, 2022.
[abs][pdf][bib]

A Primer for Neural Arithmetic Logic Modules: Bhumika Mistry, Katayoun Farrahi, Jonathon Hare; (185):1−58, 2022.
[abs][pdf][bib] [code]

Estimating Density Models with Truncation Boundaries using Score Matching: Song Liu, Takafumi Kanamori, Daniel J. Williams; (186):1−38, 2022.
[abs][pdf][bib] [code]

Adversarial Classification: Necessary Conditions and Geometric Flows: Nicolás García Trillos, Ryan Murray; (187):1−38, 2022.
[abs][pdf][bib]

Active Structure Learning of Bayesian Networks in an Observational Setting: Noa Ben-David, Sivan Sabato; (188):1−38, 2022.
[abs][pdf][bib] [code]

Learning to Optimize: A Primer and A Benchmark: Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin; (189):1−59, 2022.
[abs][pdf][bib] [code]

Clustering with Semidefinite Programming and Fixed Point Iteration: Pedro Felzenszwalb, Caroline Klivans, Alice Paul; (190):1−23, 2022.
[abs][pdf][bib]

Deep Limits and a Cut-Off Phenomenon for Neural Networks: Benny Avelin, Anders Karlsson; (191):1−29, 2022.
[abs][pdf][bib]

A Bregman Learning Framework for Sparse Neural Networks: Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger; (192):1−43, 2022.
[abs][pdf][bib] [code]

Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression: Wenjia Wang, Bing-Yi Jing; (193):1−67, 2022.
[abs][pdf][bib]

Uniform deconvolution for Poisson Point Processes: Anna Bonnet, Claire Lacour, Franck Picard, Vincent Rivoirard; (194):1−36, 2022.
[abs][pdf][bib]

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality: Yang Yu, Shih-Kang Chao, Guang Cheng; (195):1−77, 2022.
[abs][pdf][bib] [code]

Universal Approximation Theorems for Differentiable Geometric Deep Learning: Anastasis Kratsios, Léonie Papon; (196):1−73, 2022.
[abs][pdf][bib]

InterpretDL: Explaining Deep Models in PaddlePaddle: Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Zeyu Chen, Dejing Dou; (197):1−6, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions: Subha Maity, Yuekai Sun, Moulinath Banerjee; (198):1−50, 2022.
[abs][pdf][bib] [code]

A Forward Approach for Sufficient Dimension Reduction in Binary Classification: Jongkyeong Kang, Seung Jun Shin; (199):1−31, 2022.
[abs][pdf][bib]

A Nonconvex Framework for Structured Dynamic Covariance Recovery: Katherine Tsai, Mladen Kolar, Oluwasanmi Koyejo; (200):1−91, 2022.
[abs][pdf][bib] [code]

Three rates of convergence or separation via U-statistics in a dependent framework: Quentin Duchemin, Yohann De Castro, Claire Lacour; (201):1−59, 2022.
[abs][pdf][bib] [code]

abess: A Fast Best-Subset Selection Library in Python and R: Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu; (202):1−7, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Testing Whether a Learning Procedure is Calibrated: Jon Cockayne, Matthew M. Graham, Chris J. Oates, T. J. Sullivan, Onur Teymur; (203):1−36, 2022.
[abs][pdf][bib]

Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable: Baoluo Sun, Yifan Cui, Eric Tchetgen Tchetgen; (204):1−40, 2022.
[abs][pdf][bib]

Contraction rates for sparse variational approximations in Gaussian process regression: Dennis Nieman, Botond Szabo, Harry van Zanten; (205):1−26, 2022.
[abs][pdf][bib]

Stochastic DCA with Variance Reduction and Applications in Machine Learning: Hoai An Le Thi, Hoang Phuc Hau Luu, Hoai Minh Le, Tao Pham Dinh; (206):1−44, 2022.
[abs][pdf][bib]

Nonconvex Matrix Completion with Linearly Parameterized Factors: Ji Chen, Xiaodong Li, Zongming Ma; (207):1−35, 2022.
[abs][pdf][bib]

tntorch: Tensor Network Learning with PyTorch: Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler; (208):1−6, 2022.
[abs][pdf][bib] [code]

Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs: Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long; (209):1−47, 2022.
[abs][pdf][bib] [code]

A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review: Michael Pearce, Elena A. Erosheva; (210):1−33, 2022.
[abs][pdf][bib]

Efficient Inference for Dynamic Flexible Interactions of Neural Populations: Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu; (211):1−49, 2022.
[abs][pdf][bib]

Multi-Agent Multi-Armed Bandits with Limited Communication: Mridul Agarwal, Vaneet Aggarwal, Kamyar Azizzadenesheli; (212):1−24, 2022.
[abs][pdf][bib]

Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features: Lars H. B. Olsen, Ingrid K. Glad, Martin Jullum, Kjersti Aas; (213):1−51, 2022.
[abs][pdf][bib] [code]

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint: Yoav Freund, Yi-An Ma, Tong Zhang; (214):1−32, 2022.
[abs][pdf][bib]

Learning Operators with Coupled Attention: Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris; (215):1−63, 2022.
[abs][pdf][bib]

Kernel Partial Correlation Coefficient --- a Measure of Conditional Dependence: Zhen Huang, Nabarun Deb, Bodhisattva Sen; (216):1−58, 2022.
[abs][pdf][bib]

Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee: Bo Shen, Weijun Xie, Zhenyu (James) Kong; (217):1−40, 2022.
[abs][pdf][bib] [code]

Learning Green's functions associated with time-dependent partial differential equations: Nicolas Boullé, Seick Kim, Tianyi Shi, Alex Townsend; (218):1−34, 2022.
[abs][pdf][bib]

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs: Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag; (219):1−62, 2022.
[abs][pdf][bib] [code]

Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks: Alireza Fallah, Mert Gürbüzbalaban, Asuman Ozdaglar, Umut Şimşekli, Lingjiong Zhu; (220):1−96, 2022.
[abs][pdf][bib]

Behavior Priors for Efficient Reinforcement Learning: Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess; (221):1−68, 2022.
[abs][pdf][bib]

Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization: Huan Li, Zhouchen Lin, Yongchun Fang; (222):1−41, 2022.
[abs][pdf][bib]

On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping: Qiang Zhou, Sinno Jialin Pan; (223):1−59, 2022.
[abs][pdf][bib]

Getting Better from Worse: Augmented Bagging and A Cautionary Tale of Variable Importance: Lucas Mentch, Siyu Zhou; (224):1−32, 2022.
[abs][pdf][bib]

Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions: Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh; (225):1−48, 2022.
[abs][pdf][bib]

Underspecification Presents Challenges for Credibility in Modern Machine Learning: Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley; (226):1−61, 2022.
[abs][pdf][bib]

Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits: Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti; (227):1−59, 2022.
[abs][pdf][bib]

Asymptotic Study of Stochastic Adaptive Algorithms in Non-convex Landscape: Sébastien Gadat, Ioana Gavra; (228):1−54, 2022.
[abs][pdf][bib]

Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration: Congliang Chen, Li Shen, Fangyu Zou, Wei Liu; (229):1−47, 2022.
[abs][pdf][bib]

Multi-Task Dynamical Systems: Alex Bird, Christopher K. I. Williams, Christopher Hawthorne; (230):1−52, 2022.
[abs][pdf][bib]

Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation: Hiroaki Sasaki, Takashi Takenouchi; (231):1−55, 2022.
[abs][pdf][bib]

Gaussian Process Boosting: Fabio Sigrist; (232):1−46, 2022.
[abs][pdf][bib] [code]

An Efficient Sampling Algorithm for Non-smooth Composite Potentials: Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett; (233):1−50, 2022.
[abs][pdf][bib]

Change point localization in dependent dynamic nonparametric random dot product graphs: Oscar Hernan Madrid Padilla, Yi Yu, Carey E. Priebe; (234):1−59, 2022.
[abs][pdf][bib]

Bounding the Error of Discretized Langevin Algorithms for Non-Strongly Log-Concave Targets: Arnak S. Dalalyan, Avetik Karagulyan, Lionel Riou-Durand; (235):1−38, 2022.
[abs][pdf][bib]

KoPA: Automated Kronecker Product Approximation: Chencheng Cai, Rong Chen, Han Xiao; (236):1−44, 2022.
[abs][pdf][bib]

Nonparametric Principal Subspace Regression: Yang Zhou, Mark Koudstaal, Dengdeng Yu, Dehan Kong, Fang Yao; (237):1−28, 2022.
[abs][pdf][bib]

A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates: Prashanth L.A., Sanjay P. Bhat; (238):1−61, 2022.
[abs][pdf][bib]

Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization: Zhize Li, Jian Li; (239):1−61, 2022.
[abs][pdf][bib]

MALTS: Matching After Learning to Stretch: Harsh Parikh, Cynthia Rudin, Alexander Volfovsky; (240):1−42, 2022.
[abs][pdf][bib] [code]

Weakly Supervised Disentangled Generative Causal Representation Learning: Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang; (241):1−55, 2022.
[abs][pdf][bib] [code]

Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure: Yang Ni, Francesco C. Stingo, Veerabhadran Baladandayuthapani; (242):1−29, 2022.
[abs][pdf][bib]

Tree-based Node Aggregation in Sparse Graphical Models: Ines Wilms, Jacob Bien; (243):1−36, 2022.
[abs][pdf][bib] [code]

Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables: Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez; (244):1−54, 2022.
[abs][pdf][bib]

Mappings for Marginal Probabilities with Applications to Models in Statistical Physics: Mehdi Molkaraie; (245):1−36, 2022.
[abs][pdf][bib]

Multivariate Boosted Trees and Applications to Forecasting and Control: Lorenzo Nespoli, Vasco Medici; (246):1−47, 2022.
[abs][pdf][bib] [code]

Quantile regression with ReLU Networks: Estimators and minimax rates: Oscar Hernan Madrid Padilla, Wesley Tansey, Yanzhen Chen; (247):1−42, 2022.
[abs][pdf][bib] [code]

Double Spike Dirichlet Priors for Structured Weighting: Huiming Lin, Meng Li; (248):1−28, 2022.
[abs][pdf][bib] [code]

Projected Robust PCA with Application to Smooth Image Recovery: Long Feng, Junhui Wang; (249):1−41, 2022.
[abs][pdf][bib]

Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials: Daiqi Gao, Yufeng Liu, Donglin Zeng; (250):1−42, 2022.
[abs][pdf][bib]

Using Active Queries to Infer Symmetric Node Functions of Graph Dynamical Systems: Abhijin Adiga, Chris J. Kuhlman, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns; (251):1−43, 2022.
[abs][pdf][bib]

A Closer Look at Embedding Propagation for Manifold Smoothing: Diego Velazquez, Pau Rodriguez, Josep M. Gonfaus, F. Xavier Roca, Jordi Gonzalez; (252):1−27, 2022.
[abs][pdf][bib]

Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences: Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White; (253):1−79, 2022.
[abs][pdf][bib]

Adaptive Greedy Algorithm for Moderately Large Dimensions in Kernel Conditional Density Estimation: Minh-Lien Jeanne Nguyen, Claire Lacour, Vincent Rivoirard; (254):1−74, 2022.
[abs][pdf][bib]

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States: Shi Dong, Benjamin Van Roy, Zhengyuan Zhou; (255):1−54, 2022.
[abs][pdf][bib]

On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems: Michael Muehlebach, Michael I. Jordan; (256):1−47, 2022.
[abs][pdf][bib] [code]

Sparse Continuous Distributions and Fenchel-Young Losses: André F. T. Martins, Marcos Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae; (257):1−74, 2022.
[abs][pdf][bib] [code]

Tree-Based Models for Correlated Data: Assaf Rabinowicz, Saharon Rosset; (258):1−31, 2022.
[abs][pdf][bib]

Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models: Shiwei Lan; (259):1−53, 2022.
[abs][pdf][bib] [code]

A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions: Arnulf Jentzen, Adrian Riekert; (260):1−50, 2022.
[abs][pdf][bib]

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning: Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter; (261):1−61, 2022.
[abs][pdf][bib] [code]

Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score: Muxuan Liang, Young-Geun Choi, Yang Ning, Maureen A Smith, Ying-Qi Zhao; (262):1−65, 2022.
[abs][pdf][bib] [code]

The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks: Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett; (263):1−48, 2022.
[abs][pdf][bib]

A Random Matrix Perspective on Random Tensors: José Henrique de M. Goulart, Romain Couillet, Pierre Comon; (264):1−36, 2022.
[abs][pdf][bib]

Stochastic subgradient for composite convex optimization with functional constraints: Ion Necoara, Nitesh Kumar Singh; (265):1−35, 2022.
[abs][pdf][bib]

Functional Linear Regression with Mixed Predictors: Daren Wang, Zifeng Zhao, Yi Yu, Rebecca Willett; (266):1−94, 2022.
[abs][pdf][bib] [code]

Tianshou: A Highly Modularized Deep Reinforcement Learning Library: Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, Jun Zhu; (267):1−6, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

A Computationally Efficient Framework for Vector Representation of Persistence Diagrams: Kit C Chan, Umar Islambekov, Alexey Luchinsky, Rebecca Sanders; (268):1−33, 2022.
[abs][pdf][bib]

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes: Ruixuan Zhao, Xin He, Junhui Wang; (269):1−34, 2022.
[abs][pdf][bib]

Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling: Keru Wu, Scott Schmidler, Yuansi Chen; (270):1−63, 2022.
[abs][pdf][bib]

Fast Stagewise Sparse Factor Regression: Kun Chen, Ruipeng Dong, Wanwan Xu, Zemin Zheng; (271):1−45, 2022.
[abs][pdf][bib]

Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees: Kean Ming Tan, Heather Battey, Wen-Xin Zhou; (272):1−61, 2022.
[abs][pdf][bib]

The Weighted Generalised Covariance Measure: Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann; (273):1−68, 2022.
[abs][pdf][bib]

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms: Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, João G.M. Araújo; (274):1−18, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms: Yanwei Jia, Xun Yu Zhou; (275):1−50, 2022.
[abs][pdf][bib] [code]

Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons: Shijun Zhang, Zuowei Shen, Haizhao Yang; (276):1−60, 2022.
[abs][pdf][bib]

Nonstochastic Bandits with Composite Anonymous Feedback: Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Claudio Gentile, Yishay Mansour; (277):1−24, 2022.
[abs][pdf][bib]

Jump Gaussian Process Model for Estimating Piecewise Continuous Regression Functions: Chiwoo Park; (278):1−37, 2022.
[abs][pdf][bib] [code]

Convergence Guarantees for the Good-Turing Estimator: Amichai Painsky; (279):1−37, 2022.
[abs][pdf][bib]

Generalized Resubstitution for Classification Error Estimation: Parisa Ghane, Ulisses Braga-Neto; (280):1−30, 2022.
[abs][pdf][bib]

Nonparametric adaptive control and prediction: theory and randomized algorithms: Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine; (281):1−46, 2022.
[abs][pdf][bib]

On the Convergence Rates of Policy Gradient Methods: Lin Xiao; (282):1−36, 2022.
[abs][pdf][bib]

De-Sequentialized Monte Carlo: a parallel-in-time particle smoother: Adrien Corenflos, Nicolas Chopin, Simo Särkkä; (283):1−39, 2022.
[abs][pdf][bib] [code]

Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation: Chuyang Ke, Jean Honorio; (284):1−28, 2022.
[abs][pdf][bib]

Deepchecks: A Library for Testing and Validating Machine Learning Models and Data: Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach; (285):1−6, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Integral Autoencoder Network for Discretization-Invariant Learning: Yong Zheng Ong, Zuowei Shen, Haizhao Yang; (286):1−45, 2022.
[abs][pdf][bib] [code]

Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning: Haiyun He, Hanshu Yan, Vincent Y. F. Tan; (287):1−52, 2022.
[abs][pdf][bib] [code]

ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models: Francesco Martinuzzi, Chris Rackauckas, Anas Abdelrehim, Miguel D. Mahecha, Karin Mora; (288):1−8, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Estimating Causal Effects under Network Interference with Bayesian Generalized Propensity Scores: Laura Forastiere, Fabrizia Mealli, Albert Wu, Edoardo M. Airoldi; (289):1−61, 2022.
[abs][pdf][bib]

Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data: Davoud Ataee Tarzanagh, George Michailidis; (290):1−49, 2022.
[abs][pdf][bib] [code]

Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays: Lukasz Kidzinski, Francis K.C. Hui, David I. Warton, Trevor J. Hastie; (291):1−29, 2022.
[abs][pdf][bib] [code]

Two-mode Networks: Inference with as Many Parameters as Actors and Differential Privacy: Qiuping Wang, Ting Yan, Binyan Jiang, Chenlei Leng; (292):1−38, 2022.
[abs][pdf][bib]

Expected Regret and Pseudo-Regret are Equivalent When the Optimal Arm is Unique: Daron Anderson, Douglas J. Leith; (293):1−12, 2022.
[abs][pdf][bib]

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps: Bernardo Fichera, Aude Billard; (294):1−35, 2022.
[abs][pdf][bib]

Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables: Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser; (295):1−76, 2022.
[abs][pdf][bib] [code]

Stable Classification: Dimitris Bertsimas, Jack Dunn, Ivan Paskov; (296):1−53, 2022.
[abs][pdf][bib]

Handling Hard Affine SDP Shape Constraints in RKHSs: Pierre-Cyril Aubin-Frankowski, Zoltan Szabo; (297):1−54, 2022.
[abs][pdf][bib] [code]

JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data: Šimon Mandlík, Matěj Račinský, Viliam Lisý, Tomáš Pevný; (298):1−5, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Interpretable Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings: Zeda Li, Scott A. Bruce, Tian Cai; (299):1−31, 2022.
[abs][pdf][bib] [code]

More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming: Vo Nguyen Le Duy, Ichiro Takeuchi; (300):1−37, 2022.
[abs][pdf][bib] [code]

Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data: T. Tony Cai, Rong Ma; (301):1−54, 2022.
[abs][pdf][bib]

On Instrumental Variable Regression for Deep Offline Policy Evaluation: Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet; (302):1−40, 2022.
[abs][pdf][bib] [code]

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks: Alice Gatti, Zhixiong Hu, Tess Smidt, Esmond G. Ng, Pieter Ghysels; (303):1−28, 2022.
[abs][pdf][bib] [code]

Variational Inference in high-dimensional linear regression: Sumit Mukherjee, Subhabrata Sen; (304):1−56, 2022.
[abs][pdf][bib]

Tree-Values: Selective Inference for Regression Trees: Anna C. Neufeld, Lucy L. Gao, Daniela M. Witten; (305):1−43, 2022.
[abs][pdf][bib] [code]

Pathfinder: Parallel quasi-Newton variational inference: Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari; (306):1−49, 2022.
[abs][pdf][bib] [code]

Learning from Noisy Pairwise Similarity and Unlabeled Data: Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama; (307):1−34, 2022.
[abs][pdf][bib] [code]

On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations: Hong T.M. Chu, Kim-Chuan Toh, Yangjing Zhang; (308):1−39, 2022.
[abs][pdf][bib]

The Separation Capacity of Random Neural Networks: Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk; (309):1−47, 2022.
[abs][pdf][bib]

Detecting Latent Communities in Network Formation Models: Shujie Ma, Liangjun Su, Yichong Zhang; (310):1−61, 2022.
[abs][pdf][bib]

Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective: Tenghui Li, Guoxu Zhou, Yuning Qiu, Qibin Zhao; (311):1−50, 2022.
[abs][pdf][bib] [code]

Nystrom Regularization for Time Series Forecasting: Zirui Sun, Mingwei Dai, Yao Wang, Shao-Bo Lin; (312):1−42, 2022.
[abs][pdf][bib]

Intrinsic Dimension Estimation Using Wasserstein Distance: Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin; (313):1−37, 2022.
[abs][pdf][bib]

Oracle Complexity in Nonsmooth Nonconvex Optimization: Guy Kornowski, Ohad Shamir; (314):1−44, 2022.
[abs][pdf][bib]

d3rlpy: An Offline Deep Reinforcement Learning Library: Takuma Seno, Michita Imai; (315):1−20, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU: Tian Lan, Sunil Srinivasa, Huan Wang, Stephan Zheng; (316):1−6, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Nonparametric Neighborhood Selection in Graphical Models: Hao Dong, Yuedong Wang; (317):1−36, 2022.
[abs][pdf][bib]

Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depth: Jeff Calder, Mahmood Ettehad; (318):1−62, 2022.
[abs][pdf][bib] [code]

Self-Healing Robust Neural Networks via Closed-Loop Control: Zhuotong Chen, Qianxiao Li, Zheng Zhang; (319):1−54, 2022.
[abs][pdf][bib] [code]

Network Regression with Graph Laplacians: Yidong Zhou, Hans-Georg Müller; (320):1−41, 2022.
[abs][pdf][bib] [code]

On Low-rank Trace Regression under General Sampling Distribution: Nima Hamidi, Mohsen Bayati; (321):1−49, 2022.
[abs][pdf][bib] [code]

Community detection in sparse latent space models: Fengnan Gao, Zongming Ma, Hongsong Yuan; (322):1−50, 2022.
[abs][pdf][bib]

Convergence Rates for Gaussian Mixtures of Experts: Nhat Ho, Chiao-Yu Yang, Michael I. Jordan; (323):1−81, 2022.
[abs][pdf][bib]

Improving Bayesian Network Structure Learning in the Presence of Measurement Error: Yang Liu, Anthony C. Constantinou, Zhigao Guo; (324):1−28, 2022.
[abs][pdf][bib] [code]

On Mixup Regularization: Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert; (325):1−31, 2022.
[abs][pdf][bib]

Project and Forget: Solving Large-Scale Metric Constrained Problems: Rishi Sonthalia, Anna C. Gilbert; (326):1−54, 2022.
[abs][pdf][bib] [code]

Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence: Mattes Mollenhauer, Stefan Klus, Christof Schütte, Péter Koltai; (327):1−34, 2022.
[abs][pdf][bib]

Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima: Brian Swenson, Ryan Murray, H. Vincent Poor, Soummya Kar; (328):1−62, 2022.
[abs][pdf][bib]

Joint Continuous and Discrete Model Selection via Submodularity: Jonathan Bunton, Paulo Tabuada; (329):1−42, 2022.
[abs][pdf][bib]

ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network: Xing Fan, Marianna Pensky, Feng Yu, Teng Zhang; (330):1−46, 2022.
[abs][pdf][bib]

The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimation: Ulrike Schneider, Patrick Tardivel; (331):1−36, 2022.
[abs][pdf][bib]

Maximum sampled conditional likelihood for informative subsampling: HaiYing Wang, Jae Kwang Kim; (332):1−50, 2022.
[abs][pdf][bib]

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression: Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen; (333):1−79, 2022.
[abs][pdf][bib]

Fully General Online Imitation Learning: Michael K. Cohen, Marcus Hutter, Neel Nanda; (334):1−30, 2022.
[abs][pdf][bib]

Causal Aggregation: Estimation and Inference of Causal Effects by Constraint-Based Data Fusion: Jaime Roquero Gimenez, Dominik Rothenhäusler; (335):1−60, 2022.
[abs][pdf][bib]

Faster Randomized Interior Point Methods for Tall/Wide Linear Programs: Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, Petros Drineas; (336):1−48, 2022.
[abs][pdf][bib]

Statistical Optimality and Computational Efficiency of Nystrom Kernel PCA: Nicholas Sterge, Bharath K. Sriperumbudur; (337):1−32, 2022.
[abs][pdf][bib]

Interval-censored Hawkes processes: Marian-Andrei Rizoiu, Alexander Soen, Shidi Li, Pio Calderon, Leanne J. Dong, Aditya Krishna Menon, Lexing Xie; (338):1−84, 2022.
[abs][pdf][bib]

Early Stopping for Iterative Regularization with General Loss Functions: Ting Hu, Yunwen Lei; (339):1−36, 2022.
[abs][pdf][bib]

Fundamental Limits and Tradeoffs in Invariant Representation Learning: Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar; (340):1−49, 2022.
[abs][pdf][bib]

Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification: Chihao Zhang, Yiling Elaine Chen, Shihua Zhang, Jingyi Jessica Li; (341):1−65, 2022.
[abs][pdf][bib] [code]

SGD with Coordinate Sampling: Theory and Practice: Rémi Leluc, François Portier; (342):1−47, 2022.
[abs][pdf][bib] [code]

Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch: Shangtong Zhang, Remi Tachet des Combes, Romain Laroche; (343):1−91, 2022.
[abs][pdf][bib] [code]

Vector-Valued Least-Squares Regression under Output Regularity Assumptions: Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc; (344):1−50, 2022.
[abs][pdf][bib]

Constraint Reasoning Embedded Structured Prediction: Nan Jiang, Maosen Zhang, Willem-Jan van Hoeve, Yexiang Xue; (345):1−40, 2022.
[abs][pdf][bib] [code]

Minimax optimal approaches to the label shift problem in non-parametric settings: Subha Maity, Yuekai Sun, Moulinath Banerjee; (346):1−45, 2022.
[abs][pdf][bib]

Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks: El Mehdi Achour, François Malgouyres, Franck Mamalet; (347):1−56, 2022.
[abs][pdf][bib] [code]

Scalable Gaussian-process regression and variable selection using Vecchia approximations: Jian Cao, Joseph Guinness, Marc G. Genton, Matthias Katzfuss; (348):1−30, 2022.
[abs][pdf][bib] [code]

OMLT: Optimization & Machine Learning Toolkit: Francesco Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt, Calvin Tsay, Carl D Laird, Ruth Misener; (349):1−8, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Approximate Bayesian Computation via Classification: Yuexi Wang, Tetsuya Kaji, Veronika Rockova; (350):1−49, 2022.
[abs][pdf][bib]

Metrics of Calibration for Probabilistic Predictions: Imanol Arrieta-Ibarra, Paman Gujral, Jonathan Tannen, Mark Tygert, Cherie Xu; (351):1−54, 2022.
[abs][pdf][bib] [code]