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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]

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