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.
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[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.
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[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.
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[code]
- Deep Learning in Target Space
- Michael Fairbank, Spyridon Samothrakis, Luca Citi; (8):1−46, 2022.
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[code]
- Scaling Laws from the Data Manifold Dimension
- Utkarsh Sharma, Jared Kaplan; (9):1−34, 2022.
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[code]
- 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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[code]
- Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
- Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet; (13):1−35, 2022.
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[code]
- 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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[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.
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[code]
- Spatial Multivariate Trees for Big Data Bayesian Regression
- Michele Peruzzi, David B. Dunson; (17):1−40, 2022.
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[code]
- 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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[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.
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- Supervised Dimensionality Reduction and Visualization using Centroid-Encoder
- Tomojit Ghosh, Michael Kirby; (20):1−34, 2022.
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[code]
- Evolutionary Variational Optimization of Generative Models
- Jakob Drefs, Enrico Guiraud, Jörg Lücke; (21):1−51, 2022.
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[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.
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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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[code]
- 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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[code]
- 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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[code]
- Score Matched Neural Exponential Families for Likelihood-Free Inference
- Lorenzo Pacchiardi, Ritabrata Dutta; (38):1−71, 2022.
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[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.
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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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[code]
- 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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[code]
- 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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[code]
- 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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[code]
- 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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[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)
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[code]
- 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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[code]
- 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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[code]
- 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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[code]
- 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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[code]
- Generalized Sparse Additive Models
- Asad Haris, Noah Simon, Ali Shojaie; (70):1−56, 2022.
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[code]
- 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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[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.
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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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[code]
- 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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[code]
- 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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[code]
- 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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[code]
- 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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[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.
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[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.
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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.
[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.
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[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]
