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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.
[abs][pdf][bib]      [code]

Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions
Shaogao Lv, Heng Lian; (2):1−32, 2022.
[abs][pdf][bib]

Recovering shared structure from multiple networks with unknown edge distributions
Keith Levin, Asad Lodhia, Elizaveta Levina; (3):1−48, 2022.
[abs][pdf][bib]

Exploiting locality in high-dimensional Factorial hidden Markov models
Lorenzo Rimella, Nick Whiteley; (4):1−34, 2022.
[abs][pdf][bib]      [code]

Empirical Risk Minimization under Random Censorship
Guillaume Ausset, Stephan Clémençon, François Portier; (5):1−59, 2022.
[abs][pdf][bib]

XAI Beyond Classification: Interpretable Neural Clustering
Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou; (6):1−28, 2022.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Deep Learning in Target Space
Michael Fairbank, Spyridon Samothrakis, Luca Citi; (8):1−46, 2022.
[abs][pdf][bib]      [code]

Scaling Laws from the Data Manifold Dimension
Utkarsh Sharma, Jared Kaplan; (9):1−34, 2022.
[abs][pdf][bib]      [code]

Interpolating Predictors in High-Dimensional Factor Regression
Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp; (10):1−60, 2022.
[abs][pdf][bib]

Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes
Ali Kara, Serdar Yuksel; (11):1−46, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet; (13):1−35, 2022.
[abs][pdf][bib]      [code]

On Generalizations of Some Distance Based Classifiers for HDLSS Data
Sarbojit Roy, Soham Sarkar, Subhajit Dutta, Anil K. Ghosh; (14):1−41, 2022.
[abs][pdf][bib]

A Stochastic Bundle Method for Interpolation
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar; (15):1−57, 2022.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Spatial Multivariate Trees for Big Data Bayesian Regression
Michele Peruzzi, David B. Dunson; (17):1−40, 2022.
[abs][pdf][bib]      [code]

Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang; (18):1−68, 2022.
[abs][pdf][bib]      [code]

Universal Approximation in Dropout Neural Networks
Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies, Jaron Sanders, Albert Senen-Cerda; (19):1−46, 2022.
[abs][pdf][bib]

Supervised Dimensionality Reduction and Visualization using Centroid-Encoder
Tomojit Ghosh, Michael Kirby; (20):1−34, 2022.
[abs][pdf][bib]      [code]

Evolutionary Variational Optimization of Generative Models
Jakob Drefs, Enrico Guiraud, Jörg Lücke; (21):1−51, 2022.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]

Fast and Robust Rank Aggregation against Model Misspecification
Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama; (23):1−35, 2022.
[abs][pdf][bib]

On Biased Stochastic Gradient Estimation
Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb; (24):1−43, 2022.
[abs][pdf][bib]

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Maxime Vono, Daniel Paulin, Arnaud Doucet; (25):1−69, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Data-Derived Weak Universal Consistency
Narayana Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski; (27):1−55, 2022.
[abs][pdf][bib]

Novel Min-Max Reformulations of Linear Inverse Problems
Mohammed Rayyan Sheriff, Debasish Chatterjee; (28):1−46, 2022.
[abs][pdf][bib]

Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Kaiyi Ji, Junjie Yang, Yingbin Liang; (29):1−41, 2022.
[abs][pdf][bib]

A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Augusto Fasano, Daniele Durante; (30):1−26, 2022.
[abs][pdf][bib]

An improper estimator with optimal excess risk in misspecified density estimation and logistic regression
Jaouad Mourtada, Stéphane Gaïffas; (31):1−49, 2022.
[abs][pdf][bib]

Active Learning for Nonlinear System Identification with Guarantees
Horia Mania, Michael I. Jordan, Benjamin Recht; (32):1−30, 2022.
[abs][pdf][bib]

Model Averaging Is Asymptotically Better Than Model Selection For Prediction
Tri M. Le, Bertrand S. Clarke; (33):1−53, 2022.
[abs][pdf][bib]

SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
Weijing Tang, Jiaqi Ma, Qiaozhu Mei, Ji Zhu; (34):1−29, 2022.
[abs][pdf][bib]      [code]

Optimality and Stability in Non-Convex Smooth Games
Guojun Zhang, Pascal Poupart, Yaoliang Yu; (35):1−71, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
Matteo Pegoraro, Mario Beraha; (37):1−59, 2022.
[abs][pdf][bib]      [code]

Score Matched Neural Exponential Families for Likelihood-Free Inference
Lorenzo Pacchiardi, Ritabrata Dutta; (38):1−71, 2022.
[abs][pdf][bib]      [code]

(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.
[abs][pdf][bib]

Structure-adaptive Manifold Estimation
Nikita Puchkin, Vladimir Spokoiny; (40):1−62, 2022.
[abs][pdf][bib]

The correlation-assisted missing data estimator
Timothy I. Cannings, Yingying Fan; (41):1−49, 2022.
[abs][pdf][bib]

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li; (42):1−85, 2022.
[abs][pdf][bib]

Sampling Permutations for Shapley Value Estimation
Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes; (43):1−46, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Optimal Transport for Stationary Markov Chains via Policy Iteration
Kevin O'Connor, Kevin McGoff, Andrew B. Nobel; (45):1−52, 2022.
[abs][pdf][bib]      [code]

Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent
Wanrong Zhu, Zhipeng Lou, Wei Biao Wu; (46):1−22, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis
Zhiyan Ding, Shi Chen, Qin Li, Stephen J. Wright; (48):1−65, 2022.
[abs][pdf][bib]

Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection
Xinyi Wang, Lang Tong; (49):1−27, 2022.
[abs][pdf][bib]

Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
Luong-Ha Nguyen, James-A. Goulet; (50):1−33, 2022.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

LinCDE: Conditional Density Estimation via Lindsey's Method
Zijun Gao, Trevor Hastie; (52):1−55, 2022.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

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)
[abs][pdf][bib]      [code]

Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy
Terrance D. Savitsky, Matthew R.Williams, Jingchen Hu; (55):1−37, 2022.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

Inherent Tradeoffs in Learning Fair Representations
Han Zhao, Geoffrey J. Gordon; (57):1−26, 2022.
[abs][pdf][bib]

A Statistical Approach for Optimal Topic Model Identification
Craig M. Lewis, Francesco Grossetti; (58):1−20, 2022.
[abs][pdf][bib]

Causal Classification: Treatment Effect Estimation vs. Outcome Prediction
Carlos Fernández-Loría, Foster Provost; (59):1−35, 2022.
[abs][pdf][bib]

A Unifying Framework for Variance-Reduced Algorithms for Findings Zeroes of Monotone operators
Xun Zhang, William B. Haskell, Zhisheng Ye; (60):1−44, 2022.
[abs][pdf][bib]

Sparse Additive Gaussian Process Regression
Hengrui Luo, Giovanni Nattino, Matthew T. Pratola; (61):1−34, 2022.
[abs][pdf][bib]

The AIM and EM Algorithms for Learning from Coarse Data
Manfred Jaeger; (62):1−55, 2022.
[abs][pdf][bib]      [code]

Additive nonlinear quantile regression in ultra-high dimension
Ben Sherwood, Adam Maidman; (63):1−47, 2022.
[abs][pdf][bib]

Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra; (64):1−47, 2022.
[abs][pdf][bib]

On the Complexity of Approximating Multimarginal Optimal Transport
Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan; (65):1−43, 2022.
[abs][pdf][bib]

New Insights for the Multivariate Square-Root Lasso
Aaron J. Molstad; (66):1−52, 2022.
[abs][pdf][bib]      [code]

Are All Layers Created Equal?
Chiyuan Zhang, Samy Bengio, Yoram Singer; (67):1−28, 2022.
[abs][pdf][bib]

Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
Wei Zhu, Qiang Qiu, Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng; (68):1−45, 2022.
[abs][pdf][bib]

Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method
Alex Olshevsky; (69):1−32, 2022.
[abs][pdf][bib]      [code]

Generalized Sparse Additive Models
Asad Haris, Noah Simon, Ali Shojaie; (70):1−56, 2022.
[abs][pdf][bib]      [code]

Multiple-Splitting Projection Test for High-Dimensional Mean Vectors
Wanjun Liu, Xiufan Yu, Runze Li; (71):1−27, 2022.
[abs][pdf][bib]

Batch Normalization Preconditioning for Neural Network Training
Susanna Lange, Kyle Helfrich, Qiang Ye; (72):1−41, 2022.
[abs][pdf][bib]

A Kernel Two-Sample Test for Functional Data
George Wynne, Andrew B. Duncan; (73):1−51, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]

Joint Inference of Multiple Graphs from Matrix Polynomials
Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra; (76):1−35, 2022.
[abs][pdf][bib]

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec; (77):1−40, 2022.
[abs][pdf][bib]      [code]

Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos; (78):1−49, 2022.
[abs][pdf][bib]

Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
Yuling Yao, Aki Vehtari, Andrew Gelman; (79):1−45, 2022.
[abs][pdf][bib]

Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures
Marta Catalano, Pierpaolo De Blasi, Antonio Lijoi, Igor Pruenster; (80):1−23, 2022.
[abs][pdf][bib]

Dependent randomized rounding for clustering and partition systems with knapsack constraints
David G. Harris, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh; (81):1−41, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping
Yichi Zhang, Molei Liu, Matey Neykov, Tianxi Cai; (83):1−25, 2022.
[abs][pdf][bib]      [code]

Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior
Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky, Sanvesh Srivastava; (84):1−59, 2022.
[abs][pdf][bib]

A Distribution Free Conditional Independence Test with Applications to Causal Discovery
Zhanrui Cai, Runze Li, Yaowu Zhang; (85):1−41, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]

CD-split and HPD-split: Efficient Conformal Regions in High Dimensions
Rafael Izbicki, Gilson Shimizu, Rafael B. Stern; (87):1−32, 2022.
[abs][pdf][bib]      [code]

Generalized Ambiguity Decomposition for Ranking Ensemble Learning
Hongzhi Liu, Yingpeng Du, Zhonghai Wu; (88):1−36, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling
Xi Chen, Bo Jiang, Tianyi Lin, Shuzhong Zhang; (90):1−38, 2022.
[abs][pdf][bib]

When Hardness of Approximation Meets Hardness of Learning
Eran Malach, Shai Shalev-Shwartz; (91):1−24, 2022.
[abs][pdf][bib]

Gauss-Legendre Features for Gaussian Process Regression
Paz Fink Shustin, Haim Avron; (92):1−47, 2022.
[abs][pdf][bib]

Regularized K-means Through Hard-Thresholding
Jakob Raymaekers, Ruben H. Zamar; (93):1−48, 2022.
[abs][pdf][bib]      [code]

Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach
Kweku Abraham, Ismaël Castillo, Elisabeth Gassiat; (94):1−57, 2022.
[abs][pdf][bib]

Attraction-Repulsion Spectrum in Neighbor Embeddings
Jan Niklas Böhm, Philipp Berens, Dmitry Kobak; (95):1−32, 2022.
[abs][pdf][bib]      [code]

Rethinking Nonlinear Instrumental Variable Models through Prediction Validity
Chunxiao Li, Cynthia Rudin, Tyler H. McCormick; (96):1−55, 2022.
[abs][pdf][bib]

Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective
Daniel Sanz-Alonso, Ruiyi Yang; (97):1−28, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Distributed Learning of Finite Gaussian Mixtures
Qiong Zhang, Jiahua Chen; (99):1−40, 2022.
[abs][pdf][bib]      [code]

Total Stability of SVMs and Localized SVMs
Hannes Köhler, Andreas Christmann; (100):1−41, 2022.
[abs][pdf][bib]

Towards An Efficient Approach for the Nonconvex lp Ball Projection: Algorithm and Analysis
Xiangyu Yang, Jiashan Wang, Hao Wang; (101):1−31, 2022.
[abs][pdf][bib]

Sufficient reductions in regression with mixed predictors
Efstathia Bura, Liliana Forzani, Rodrigo Garcia Arancibia, Pamela Llop, Diego Tomassi; (102):1−47, 2022.
[abs][pdf][bib]      [code]

The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures
Nir Weinberger, Guy Bresler; (103):1−79, 2022.
[abs][pdf][bib]

Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency
F. Richard Guo, Emilija Perković; (104):1−41, 2022.
[abs][pdf][bib]      [code]

Globally Injective ReLU Networks
Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanić, Maarten de Hoop; (105):1−55, 2022.
[abs][pdf][bib]

Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold
Bokun Wang, Shiqian Ma, Lingzhou Xue; (106):1−33, 2022.
[abs][pdf][bib]

IALE: Imitating Active Learner Ensembles
Christoffer Löffler, Christopher Mutschler; (107):1−29, 2022.
[abs][pdf][bib]      [code]

Bayesian subset selection and variable importance for interpretable prediction and classification
Daniel R. Kowal; (108):1−38, 2022.
[abs][pdf][bib]      [code]

Conditions and Assumptions for Constraint-based Causal Structure Learning
Kayvan Sadeghi, Terry Soo; (109):1−34, 2022.
[abs][pdf][bib]

EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation
Jun Ho Yoon, Seyoung Kim; (110):1−39, 2022.
[abs][pdf][bib]      [code]

Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces
Masaaki Imaizumi, Kenji Fukumizu; (111):1−54, 2022.
[abs][pdf][bib]

Sum of Ranked Range Loss for Supervised Learning
Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu; (112):1−44, 2022.
[abs][pdf][bib]      [code]

The Two-Sided Game of Googol
José Correa, Andrés Cristi, Boris Epstein, José Soto; (113):1−37, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Cauchy–Schwarz Regularized Autoencoder
Linh Tran, Maja Pantic, Marc Peter Deisenroth; (115):1−37, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Under-bagging Nearest Neighbors for Imbalanced Classification
Hanyuan Hang, Yuchao Cai, Hanfang Yang, Zhouchen Lin; (118):1−63, 2022.
[abs][pdf][bib]

A spectral-based analysis of the separation between two-layer neural networks and linear methods
Lei Wu, Jihao Long; (119):1−34, 2022.
[abs][pdf][bib]

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
William Fedus, Barret Zoph, Noam Shazeer; (120):1−39, 2022.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]

Depth separation beyond radial functions
Luca Venturi, Samy Jelassi, Tristan Ozuch, Joan Bruna; (122):1−56, 2022.
[abs][pdf][bib]

Provable Tensor-Train Format Tensor Completion by Riemannian Optimization
Jian-Feng Cai, Jingyang Li, Dong Xia; (123):1−77, 2022.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

Foolish Crowds Support Benign Overfitting
Niladri S. Chatterji, Philip M. Long; (125):1−12, 2022.
[abs][pdf][bib]

Neural Estimation of Statistical Divergences
Sreejith Sreekumar, Ziv Goldfeld; (126):1−75, 2022.
[abs][pdf][bib]

Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
Haoyuan Chen, Liang Ding, Rui Tuo; (127):1−32, 2022.
[abs][pdf][bib]

Power Iteration for Tensor PCA
Jiaoyang Huang, Daniel Z. Huang, Qing Yang, Guang Cheng; (128):1−47, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli; (130):1−55, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference
Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas; (132):1−109, 2022.
[abs][pdf][bib]      [code]

Manifold Coordinates with Physical Meaning
Samson J. Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen; (133):1−57, 2022.
[abs][pdf][bib]      [code]

Transfer Learning in Information Criteria-based Feature Selection
Shaohan Chen, Nikolaos V. Sahinidis, Chuanhou Gao; (134):1−105, 2022.
[abs][pdf][bib]      [code]

Recovery and Generalization in Over-Realized Dictionary Learning
Jeremias Sulam, Chong You, Zhihui Zhu; (135):1−23, 2022.
[abs][pdf][bib]

Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok; (136):1−60, 2022.
[abs][pdf][bib]      [code]

On the Efficiency of Entropic Regularized Algorithms for Optimal Transport
Tianyi Lin, Nhat Ho, Michael I. Jordan; (137):1−42, 2022.
[abs][pdf][bib]

Exact simulation of diffusion first exit times: algorithm acceleration
Samuel Herrmann, Cristina Zucca; (138):1−20, 2022.
[abs][pdf][bib]      [code]

No Weighted-Regret Learning in Adversarial Bandits with Delays
Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet; (139):1−43, 2022.
[abs][pdf][bib]

Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems
Yahya Sattar, Samet Oymak; (140):1−49, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]

A Perturbation-Based Kernel Approximation Framework
Roy Mitz, Yoel Shkolnisky; (142):1−26, 2022.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Improved Classification Rates for Localized SVMs
Ingrid Blaschzyk, Ingo Steinwart; (165):1−59, 2022.
[abs][pdf][bib]

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]

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