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JMLR Volume 21

A Low Complexity Algorithm with O(√T) Regret and O(1) Constraint Violations for Online Convex Optimization with Long Term Constraints
Hao Yu, Michael J. Neely; (1):1−24, 2020.
[abs][pdf][bib]

A Statistical Learning Approach to Modal Regression
Yunlong Feng, Jun Fan, Johan A.K. Suykens; (2):1−35, 2020.
[abs][pdf][bib]

A Model of Fake Data in Data-driven Analysis
Xiaofan Li, Andrew B. Whinston; (3):1−26, 2020.
[abs][pdf][bib]

Universal Latent Space Model Fitting for Large Networks with Edge Covariates
Zhuang Ma, Zongming Ma, Hongsong Yuan; (4):1−67, 2020.
[abs][pdf][bib]

Lower Bounds for Parallel and Randomized Convex Optimization
Jelena Diakonikolas, Cristóbal Guzmán; (5):1−31, 2020.
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Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms
Anna Little, Mauro Maggioni, James M. Murphy; (6):1−66, 2020.
[abs][pdf][bib]      [code]

Target Propagation in Recurrent Neural Networks
Nikolay Manchev, Michael Spratling; (7):1−33, 2020.
[abs][pdf][bib]      [code]

DESlib: A Dynamic ensemble selection library in Python
Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti; (8):1−5, 2020.
[abs][pdf][bib]      [code]

On Mahalanobis Distance in Functional Settings
José R. Berrendero, Beatriz Bueno-Larraz, Antonio Cuevas; (9):1−33, 2020.
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Online Sufficient Dimension Reduction Through Sliced Inverse Regression
Zhanrui Cai, Runze Li, Liping Zhu; (10):1−25, 2020.
[abs][pdf][bib]

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin; (11):1−34, 2020.
[abs][pdf][bib]

Neyman-Pearson classification: parametrics and sample size requirement
Xin Tong, Lucy Xia, Jiacheng Wang, Yang Feng; (12):1−48, 2020.
[abs][pdf][bib]      [code]

Generalized probabilistic principal component analysis of correlated data
Mengyang Gu, Weining Shen; (13):1−41, 2020.
[abs][pdf][bib]      [code]

On lp-Support Vector Machines and Multidimensional Kernels
Victor Blanco, Justo Puerto, Antonio M. Rodriguez-Chia; (14):1−29, 2020.
[abs][pdf][bib]

Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
Ery Arias-Castro, Adel Javanmard, Bruno Pelletier; (15):1−37, 2020.
[abs][pdf][bib]

Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta; (16):1−42, 2020.
[abs][pdf][bib]

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data
Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert; (17):1−53, 2020.
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Connecting Spectral Clustering to Maximum Margins and Level Sets
David P. Hofmeyr; (18):1−35, 2020.
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High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix
Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong; (19):1−25, 2020.
[abs][pdf][bib]

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections
Junhong Lin, Volkan Cevher; (20):1−44, 2020.
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Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright; (21):1−51, 2020.
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A Unified Framework for Structured Graph Learning via Spectral Constraints
Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar; (22):1−60, 2020.
[abs][pdf][bib]      [code]

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu; (23):1−7, 2020.
[abs][pdf][bib]

Distributed Feature Screening via Componentwise Debiasing
Xingxiang Li, Runze Li, Zhiming Xia, Chen Xu; (24):1−32, 2020.
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Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models
Ivona Bezáková, Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda; (25):1−62, 2020.
[abs][pdf][bib]

Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes
Anders Ellern Bilgrau, Carel F.W. Peeters, Poul Svante Eriksen, Martin Boegsted, Wessel N. van Wieringen; (26):1−52, 2020.
[abs][pdf][bib]

A New Class of Time Dependent Latent Factor Models with Applications
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien; (27):1−24, 2020.
[abs][pdf][bib]

On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms
Nicolas Garcia Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso; (28):1−47, 2020.
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The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response
Xin Zhang, Qing Mai, Hui Zou; (29):1−36, 2020.
[abs][pdf][bib]

Tensor Train Decomposition on TensorFlow (T3F)
Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets; (30):1−7, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Generalized Nonbacktracking Bounds on the Influence
Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee; (31):1−36, 2020.
[abs][pdf][bib]

Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
Mihai Cucuringu, Hemant Tyagi; (32):1−77, 2020.
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On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent
Huan Li, Zhouchen Lin; (33):1−45, 2020.
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Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent
Dominic Richards, Patrick Rebeschini; (34):1−44, 2020.
[abs][pdf][bib]

Learning with Fenchel-Young losses
Mathieu Blondel, André F.T. Martins, Vlad Niculae; (35):1−69, 2020.
[abs][pdf][bib]      [code]

Noise Accumulation in High Dimensional Classification and Total Signal Index
Miriam R. Elman, Jessica Minnier, Xiaohui Chang, Dongseok Choi; (36):1−23, 2020.
[abs][pdf][bib]      [code]

Causal Discovery Toolbox: Uncovering causal relationships in Python
Diviyan Kalainathan, Olivier Goudet, Ritik Dutta; (37):1−5, 2020.
[abs][pdf][bib]      [code]

Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification
Leo L. Duan; (38):1−25, 2020.
[abs][pdf][bib]      [code]

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang; (39):1−24, 2020.
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Optimal Bipartite Network Clustering
Zhixin Zhou, Arash A. Amini; (40):1−68, 2020.
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Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables
Rune Christiansen, Jonas Peters; (41):1−46, 2020.
[abs][pdf][bib]      [code]

Branch and Bound for Piecewise Linear Neural Network Verification
Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H.S. Torr, Pushmeet Kohli, M. Pawan Kumar; (42):1−39, 2020.
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Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan; (43):1−36, 2020.
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Dynamical Systems as Temporal Feature Spaces
Peter Tino; (44):1−42, 2020.
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A Convex Parametrization of a New Class of Universal Kernel Functions
Brendon K. Colbert, Matthew M. Peet; (45):1−29, 2020.
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pyts: A Python Package for Time Series Classification
Johann Faouzi, Hicham Janati; (46):1−6, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement
Wouter Kool, Herke van Hoof, Max Welling; (47):1−36, 2020.
[abs][pdf][bib]      [code]

Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory
Thomas Ricatte, Rémi Gilleron, Marc Tommasi; (48):1−18, 2020.
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Ensemble Learning for Relational Data
Hoda Eldardiry, Jennifer Neville, Ryan A. Rossi; (49):1−37, 2020.
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Sparse and low-rank multivariate Hawkes processes
Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, Jean-Francois Muzy; (50):1−32, 2020.
[abs][pdf][bib]      [code]

Learning Causal Networks via Additive Faithfulness
Kuang-Yao Lee, Tianqi Liu, Bing Li, Hongyu Zhao; (51):1−38, 2020.
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Expected Policy Gradients for Reinforcement Learning
Kamil Ciosek, Shimon Whiteson; (52):1−51, 2020.
[abs][pdf][bib]

High-Dimensional Inference for Cluster-Based Graphical Models
Carson Eisenach, Florentina Bunea, Yang Ning, Claudiu Dinicu; (53):1−55, 2020.
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GraKeL: A Graph Kernel Library in Python
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis; (54):1−5, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Conjugate Gradients for Kernel Machines
Simon Bartels, Philipp Hennig; (55):1−42, 2020.
[abs][pdf][bib]      [code]

Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes
Peter D. Grünwald, Nishant A. Mehta; (56):1−80, 2020.
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Self-paced Multi-view Co-training
Fan Ma, Deyu Meng, Xuanyi Dong, Yi Yang; (57):1−38, 2020.
[abs][pdf][bib]      [code]

Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions
Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis; (58):1−47, 2020.
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Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis
Salar Fattahi, Somayeh Sojoudi; (59):1−51, 2020.
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Kymatio: Scattering Transforms in Python
Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg; (60):1−6, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Multiparameter Persistence Landscapes
Oliver Vipond; (61):1−38, 2020.
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Generalized Optimal Matching Methods for Causal Inference
Nathan Kallus; (62):1−54, 2020.
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Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning
Yu Wang, Siqi Wu, Bin Yu; (63):1−52, 2020.
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Community-Based Group Graphical Lasso
Eugen Pircalabelu, Gerda Claeskens; (64):1−32, 2020.
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Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients
Yu Liu, Kris De Brabanter; (65):1−45, 2020.
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WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions
Edgar Dobriban, Yue Sheng; (66):1−52, 2020.
[abs][pdf][bib]      [code]

The weight function in the subtree kernel is decisive
Romain Azaïs, Florian Ingels; (67):1−36, 2020.
[abs][pdf][bib]      [code]

On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
Xi Chen, Simon S. Du, Xin T. Tong; (68):1−41, 2020.
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Union of Low-Rank Tensor Spaces: Clustering and Completion
Morteza Ashraphijuo, Xiaodong Wang; (69):1−36, 2020.
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Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart; (70):1−73, 2020.
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Estimation of a Low-rank Topic-Based Model for Information Cascades
Ming Yu, Varun Gupta, Mladen Kolar; (71):1−47, 2020.
[abs][pdf][bib]      [code]

(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, Olga Mineeva; (72):1−22, 2020.
[abs][pdf][bib]      [code]

Scalable Approximate MCMC Algorithms for the Horseshoe Prior
James Johndrow, Paulo Orenstein, Anirban Bhattacharya; (73):1−61, 2020.
[abs][pdf][bib]

High-dimensional Gaussian graphical models on network-linked data
Tianxi Li, Cheng Qian, Elizaveta Levina, Ji Zhu; (74):1−45, 2020.
[abs][pdf][bib]      [code]

Identifiability of Additive Noise Models Using Conditional Variances
Gunwoong Park; (75):1−34, 2020.
[abs][pdf][bib]

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi, Mehdi Bennis, Vaneet Aggarwal; (76):1−39, 2020.
[abs][pdf][bib]

Multi-Player Bandits: The Adversarial Case
Pragnya Alatur, Kfir Y. Levy, Andreas Krause; (77):1−23, 2020.
[abs][pdf][bib]

Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms
Malte Probst, Franz Rothlauf; (78):1−31, 2020.
[abs][pdf][bib]      [code]

Quantile Graphical Models: a Bayesian Approach
Nilabja Guha, Veera Baladandayuthapani, Bani K. Mallick; (79):1−47, 2020.
[abs][pdf][bib]

Memoryless Sequences for General Losses
Rafael Frongillo, Andrew Nobel; (80):1−28, 2020.
[abs][pdf][bib]

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing; (81):1−27, 2020.
[abs][pdf][bib]      [code]

Sequential change-point detection in high-dimensional Gaussian graphical models
Hossein Keshavarz, George Michaildiis, Yves Atchade; (82):1−57, 2020.
[abs][pdf][bib]

Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis
Xiaoming Yuan, Shangzhi Zeng, Jin Zhang; (83):1−75, 2020.
[abs][pdf][bib]

Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions
Christiane Görgen, Manuele Leonelli; (84):1−32, 2020.
[abs][pdf][bib]

Effective Ways to Build and Evaluate Individual Survival Distributions
Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner; (85):1−63, 2020.
[abs][pdf][bib]      [code]

Convergence Rate of Optimal Quantization and Application to the Clustering Performance of the Empirical Measure
Yating Liu, Gilles Pagès; (86):1−36, 2020.
[abs][pdf][bib]

Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data
Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque; (87):1−40, 2020.
[abs][pdf][bib]      [code]

Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators
Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi; (88):1−54, 2020.
[abs][pdf][bib]      [code]

Causal Discovery from Heterogeneous/Nonstationary Data
Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf; (89):1−53, 2020.
[abs][pdf][bib]      [code]

Probabilistic Symmetries and Invariant Neural Networks
Benjamin Bloem-Reddy, Yee Whye Teh; (90):1−61, 2020.
[abs][pdf][bib]

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching
Ming Yu, Varun Gupta, Mladen Kolar; (91):1−51, 2020.
[abs][pdf][bib]

Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright, Bin Yu; (92):1−71, 2020.
[abs][pdf][bib]

Distributed Kernel Ridge Regression with Communications
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou; (93):1−38, 2020.
[abs][pdf][bib]      [code]

Minimax Nonparametric Parallelism Test
Xin Xing, Meimei Liu, Ping Ma, Wenxuan Zhong; (94):1−47, 2020.
[abs][pdf][bib]      [code]

Cornac: A Comparative Framework for Multimodal Recommender Systems
Aghiles Salah, Quoc-Tuan Truong, Hady W. Lauw; (95):1−5, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

pyDML: A Python Library for Distance Metric Learning
Juan Luis Suárez, Salvador García, Francisco Herrera; (96):1−7, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Loss Control with Rank-one Covariance Estimate for Short-term Portfolio Optimization
Zhao-Rong Lai, Liming Tan, Xiaotian Wu, Liangda Fang; (97):1−37, 2020.
[abs][pdf][bib]      [code]

A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings
Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi; (98):1−67, 2020.
[abs][pdf][bib]

Joint Causal Inference from Multiple Contexts
Joris M. Mooij, Sara Magliacane, Tom Claassen; (99):1−108, 2020.
[abs][pdf][bib]      [code]

General Latent Feature Models for Heterogeneous Datasets
Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani; (100):1−49, 2020.
[abs][pdf][bib]      [code]

Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size
Francois Kamper, Sarel J. Steel, Johan A. du Preez; (101):1−42, 2020.
[abs][pdf][bib]

AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)
Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé; (102):1−12, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Stochastic Nested Variance Reduction for Nonconvex Optimization
Dongruo Zhou, Pan Xu, Quanquan Gu; (103):1−63, 2020.
[abs][pdf][bib]

Sparse Projection Oblique Randomer Forests
Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Carey E. Priebe, Jason Yim, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein; (104):1−39, 2020.
[abs][pdf][bib]      [code]

Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
Aryan Mokhtari, Hamed Hassani, Amin Karbasi; (105):1−49, 2020.
[abs][pdf][bib]

Quadratic Decomposable Submodular Function Minimization: Theory and Practice
Pan Li, Niao He, Olgica Milenkovic; (106):1−49, 2020.
[abs][pdf][bib]      [code]

Change Point Estimation in a Dynamic Stochastic Block Model
Monika Bhattacharjee, Moulinath Banerjee, George Michailidis; (107):1−59, 2020.
[abs][pdf][bib]

ThunderGBM: Fast GBDTs and Random Forests on GPUs
Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (108):1−5, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Bayesian Model Selection with Graph Structured Sparsity
Youngseok Kim, Chao Gao; (109):1−61, 2020.
[abs][pdf][bib]

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh; (110):1−48, 2020.
[abs][pdf][bib]      [code]

MFE: Towards reproducible meta-feature extraction
Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luís P. F. Garcia, Jefferson T. Oliva, André C. P. L. F. de Carvalho; (111):1−5, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model
Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini; (112):1−24, 2020.
[abs][pdf][bib]

Prediction regions through Inverse Regression
Emilie Devijver, Emeline Perthame; (113):1−24, 2020.
[abs][pdf][bib]      [code]

NEVAE: A Deep Generative Model for Molecular Graphs
Bidisha Samanta, Abir De, Gourhari Jana, Vicenç Gómez, Pratim Chattaraj, Niloy Ganguly, Manuel Gomez-Rodriguez; (114):1−33, 2020.
[abs][pdf][bib]

Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space
Elisabeth Gassiat, Sylvain Le Corff, Luc Lehéricy; (115):1−40, 2020.
[abs][pdf][bib]

GluonTS: Probabilistic and Neural Time Series Modeling in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang; (116):1−6, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models
Jiahe Lin, George Michailidis; (117):1−51, 2020.
[abs][pdf][bib]      [code]

Tslearn, A Machine Learning Toolkit for Time Series Data
Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, Eli Woods; (118):1−6, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Bayesian Closed Surface Fitting Through Tensor Products
Olivier Binette, Debdeep Pati, David B. Dunson; (119):1−26, 2020.
[abs][pdf][bib]

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
Aryan Mokhtari, Alec Koppel, Martin Takac, Alejandro Ribeiro; (120):1−51, 2020.
[abs][pdf][bib]

Agnostic Estimation for Phase Retrieval
Matey Neykov, Zhaoran Wang, Han Liu; (121):1−39, 2020.
[abs][pdf][bib]

Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering
Israel A. Almodóvar-Rivera, Ranjan Maitra; (122):1−54, 2020.
[abs][pdf][bib]      [code]

Tensor Regression Networks
Jean Kossaifi, Zachary C. Lipton, Arinbjorn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar; (123):1−21, 2020.
[abs][pdf][bib]

Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination
Hang Yu, Songwei Wu, Luyin Xin, Justin Dauwels; (124):1−54, 2020.
[abs][pdf][bib]      [code]

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh, Tim Roughgarden, Joshua R. Wang; (125):1−31, 2020.
[abs][pdf][bib]

Distributed Minimum Error Entropy Algorithms
Xin Guo, Ting Hu, Qiang Wu; (126):1−31, 2020.
[abs][pdf][bib]

Apache Mahout: Machine Learning on Distributed Dataflow Systems
Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, Özgür Yılmazel; (127):1−6, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

A Regularization-Based Adaptive Test for High-Dimensional GLMs
Chong Wu, Gongjun Xu, Xiaotong Shen, Wei Pan; (128):1−67, 2020.
[abs][pdf][bib]      [code]

A General System of Differential Equations to Model First-Order Adaptive Algorithms
Andre Belotto da Silva, Maxime Gazeau; (129):1−42, 2020.
[abs][pdf][bib]

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang; (130):1−6, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Convergence of Sparse Variational Inference in Gaussian Processes Regression
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk; (131):1−63, 2020.
[abs][pdf][bib]      [code]

Monte Carlo Gradient Estimation in Machine Learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih; (132):1−62, 2020.
[abs][pdf][bib]      [code]

Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Yao Ma, Alex Olshevsky, Csaba Szepesvari, Venkatesh Saligrama; (133):1−36, 2020.
[abs][pdf][bib]

Probabilistic Learning on Graphs via Contextual Architectures
Davide Bacciu, Federico Errica, Alessio Micheli; (134):1−39, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks
Owen Marschall, Kyunghyun Cho, Cristina Savin; (135):1−34, 2020.
[abs][pdf][bib]      [code]

Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions
Benjamin Fehrman, Benjamin Gess, Arnulf Jentzen; (136):1−48, 2020.
[abs][pdf][bib]

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang; (137):1−45, 2020.
[abs][pdf][bib]

metric-learn: Metric Learning Algorithms in Python
William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet; (138):1−6, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks
Amir R. Asadi, Emmanuel Abbe; (139):1−32, 2020.
[abs][pdf][bib]      [code]

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu; (140):1−67, 2020.
[abs][pdf][bib]      [code]

Importance Sampling Techniques for Policy Optimization
Alberto Maria Metelli, Matteo Papini, Nico Montali, Marcello Restelli; (141):1−75, 2020.
[abs][pdf][bib]      [code]

Nesterov's Acceleration for Approximate Newton
Haishan Ye, Luo Luo, Zhihua Zhang; (142):1−37, 2020.
[abs][pdf][bib]

A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang; (143):1−45, 2020.
[abs][pdf][bib]

Empirical Priors for Prediction in Sparse High-dimensional Linear Regression
Ryan Martin, Yiqi Tang; (144):1−30, 2020.
[abs][pdf][bib]

Orlicz Random Fourier Features
Linda Chamakh, Emmanuel Gobet, Zoltán Szabó; (145):1−37, 2020.
[abs][pdf][bib]

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