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 PDF BibTeX

• A Statistical Learning Approach to Modal Regression

  Yunlong Feng, Jun Fan, Johan A.K. Suykens (2):1−35, 2020 PDF BibTeX

• A Model of Fake Data in Data-driven Analysis

  Xiaofan Li, Andrew B. Whinston (3):1−26, 2020 PDF BibTeX

• Universal Latent Space Model Fitting for Large Networks with Edge Covariates

  Zhuang Ma, Zongming Ma, Hongsong Yuan (4):1−67, 2020 PDF BibTeX

• Lower Bounds for Parallel and Randomized Convex Optimization

  Jelena Diakonikolas, Cristóbal Guzmán (5):1−31, 2020 PDF BibTeX

• Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms

  Anna Little, Mauro Maggioni, James M. Murphy (6):1−66, 2020 codePDF BibTeX

• Target Propagation in Recurrent Neural Networks

  Nikolay Manchev, Michael Spratling (7):1−33, 2020 codePDF BibTeX

• 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 codePDF BibTeX

• On Mahalanobis Distance in Functional Settings

  José R. Berrendero, Beatriz Bueno-Larraz, Antonio Cuevas (9):1−33, 2020 PDF BibTeX

• Online Sufficient Dimension Reduction Through Sliced Inverse Regression

  Zhanrui Cai, Runze Li, Liping Zhu (10):1−25, 2020 PDF BibTeX

• 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 PDF BibTeX

• Neyman-Pearson classification: parametrics and sample size requirement

  Xin Tong, Lucy Xia, Jiacheng Wang, Yang Feng (12):1−48, 2020 codePDF BibTeX

• Generalized probabilistic principal component analysis of correlated data

  Mengyang Gu, Weining Shen (13):1−41, 2020 codePDF BibTeX

• On lp-Support Vector Machines and Multidimensional Kernels

  Victor Blanco, Justo Puerto, Antonio M. Rodriguez-Chia (14):1−29, 2020 PDF BibTeX

• Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning

  Ery Arias-Castro, Adel Javanmard, Bruno Pelletier (15):1−37, 2020 PDF BibTeX

• Practical Locally Private Heavy Hitters

  Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta (16):1−42, 2020 PDF BibTeX

• 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 PDF BibTeX

• Connecting Spectral Clustering to Maximum Margins and Level Sets

  David P. Hofmeyr (18):1−35, 2020 PDF BibTeX

• High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix

  Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong (19):1−25, 2020 PDF BibTeX

• Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

  Junhong Lin, Volkan Cevher (20):1−44, 2020 PDF BibTeX

• 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 PDF BibTeX

• 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 codePDF BibTeX

• 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 PDF BibTeX

• Distributed Feature Screening via Componentwise Debiasing

  Xingxiang Li, Runze Li, Zhiming Xia, Chen Xu (24):1−32, 2020 PDF BibTeX

• 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 PDF BibTeX

• 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 PDF BibTeX

• A New Class of Time Dependent Latent Factor Models with Applications

  Sinead A. Williamson, Michael Minyi Zhang, Paul Damien (27):1−24, 2020 PDF BibTeX

• 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 PDF BibTeX

• The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response

  Xin Zhang, Qing Mai, Hui Zou (29):1−36, 2020 PDF BibTeX

• Tensor Train Decomposition on TensorFlow (T3F)

  Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets (30):1−7, 2020 codePDF BibTeX

• Generalized Nonbacktracking Bounds on the Influence

  Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee (31):1−36, 2020 PDF BibTeX

• Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping

  Mihai Cucuringu, Hemant Tyagi (32):1−77, 2020 PDF BibTeX

• On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent

  Huan Li, Zhouchen Lin (33):1−45, 2020 PDF BibTeX

• Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent

  Dominic Richards, Patrick Rebeschini (34):1−44, 2020 PDF BibTeX

• Learning with Fenchel-Young losses

  Mathieu Blondel, André F.T. Martins, Vlad Niculae (35):1−69, 2020 codePDF BibTeX

• Noise Accumulation in High Dimensional Classification and Total Signal Index

  Miriam R. Elman, Jessica Minnier, Xiaohui Chang, Dongseok Choi (36):1−23, 2020 codePDF BibTeX

• Causal Discovery Toolbox: Uncovering causal relationships in Python

  Diviyan Kalainathan, Olivier Goudet, Ritik Dutta (37):1−5, 2020 codePDF BibTeX

• Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification

  Leo L. Duan (38):1−25, 2020 codePDF BibTeX

• Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables

  Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang (39):1−24, 2020 PDF BibTeX

• Optimal Bipartite Network Clustering

  Zhixin Zhou, Arash A. Amini (40):1−68, 2020 PDF BibTeX

• Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables

  Rune Christiansen, Jonas Peters (41):1−46, 2020 codePDF BibTeX

• 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 PDF BibTeX

• 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 PDF BibTeX

• Dynamical Systems as Temporal Feature Spaces

  Peter Tino (44):1−42, 2020 PDF BibTeX

• A Convex Parametrization of a New Class of Universal Kernel Functions

  Brendon K. Colbert, Matthew M. Peet (45):1−29, 2020 PDF BibTeX

• pyts: A Python Package for Time Series Classification

  Johann Faouzi, Hicham Janati (46):1−6, 2020 codePDF BibTeX

• Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement

  Wouter Kool, Herke van Hoof, Max Welling (47):1−36, 2020 codePDF BibTeX

• Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory

  Thomas Ricatte, Rémi Gilleron, Marc Tommasi (48):1−18, 2020 PDF BibTeX

• Ensemble Learning for Relational Data

  Hoda Eldardiry, Jennifer Neville, Ryan A. Rossi (49):1−37, 2020 PDF BibTeX

• Sparse and low-rank multivariate Hawkes processes

  Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, Jean-Francois Muzy (50):1−32, 2020 codePDF BibTeX

• Learning Causal Networks via Additive Faithfulness

  Kuang-Yao Lee, Tianqi Liu, Bing Li, Hongyu Zhao (51):1−38, 2020 PDF BibTeX

• Expected Policy Gradients for Reinforcement Learning

  Kamil Ciosek, Shimon Whiteson (52):1−51, 2020 PDF BibTeX

• High-Dimensional Inference for Cluster-Based Graphical Models

  Carson Eisenach, Florentina Bunea, Yang Ning, Claudiu Dinicu (53):1−55, 2020 PDF BibTeX

• GraKeL: A Graph Kernel Library in Python

  Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis (54):1−5, 2020 codePDF BibTeX

• Conjugate Gradients for Kernel Machines

  Simon Bartels, Philipp Hennig (55):1−42, 2020 codePDF BibTeX

• Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes

  Peter D. Grünwald, Nishant A. Mehta (56):1−80, 2020 PDF BibTeX

• Self-paced Multi-view Co-training

  Fan Ma, Deyu Meng, Xuanyi Dong, Yi Yang (57):1−38, 2020 codePDF BibTeX

• 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 PDF BibTeX

• 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 PDF BibTeX

• 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 codePDF BibTeX

• Multiparameter Persistence Landscapes

  Oliver Vipond (61):1−38, 2020 PDF BibTeX

• Generalized Optimal Matching Methods for Causal Inference

  Nathan Kallus (62):1−54, 2020 PDF BibTeX

• Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning

  Yu Wang, Siqi Wu, Bin Yu (63):1−52, 2020 PDF BibTeX

• Community-Based Group Graphical Lasso

  Eugen Pircalabelu, Gerda Claeskens (64):1−32, 2020 PDF BibTeX

• Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients

  Yu Liu, Kris De Brabanter (65):1−45, 2020 PDF BibTeX

• WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions

  Edgar Dobriban, Yue Sheng (66):1−52, 2020 codePDF BibTeX

• The weight function in the subtree kernel is decisive

  Romain Azaïs, Florian Ingels (67):1−36, 2020 codePDF BibTeX

• On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics

  Xi Chen, Simon S. Du, Xin T. Tong (68):1−41, 2020 PDF BibTeX

• Union of Low-Rank Tensor Spaces: Clustering and Completion

  Morteza Ashraphijuo, Xiaodong Wang (69):1−36, 2020 PDF BibTeX

• 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 PDF BibTeX

• Estimation of a Low-rank Topic-Based Model for Information Cascades

  Ming Yu, Varun Gupta, Mladen Kolar (71):1−47, 2020 codePDF BibTeX

• (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 codePDF BibTeX

• Scalable Approximate MCMC Algorithms for the Horseshoe Prior

  James Johndrow, Paulo Orenstein, Anirban Bhattacharya (73):1−61, 2020 PDF BibTeX

• High-dimensional Gaussian graphical models on network-linked data

  Tianxi Li, Cheng Qian, Elizaveta Levina, Ji Zhu (74):1−45, 2020 codePDF BibTeX

• Identifiability of Additive Noise Models Using Conditional Variances

  Gunwoong Park (75):1−34, 2020 PDF BibTeX

• 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 PDF BibTeX

• Multi-Player Bandits: The Adversarial Case

  Pragnya Alatur, Kfir Y. Levy, Andreas Krause (77):1−23, 2020 PDF BibTeX

• Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms

  Malte Probst, Franz Rothlauf (78):1−31, 2020 codePDF BibTeX

• Quantile Graphical Models: a Bayesian Approach

  Nilabja Guha, Veera Baladandayuthapani, Bani K. Mallick (79):1−47, 2020 PDF BibTeX

• Memoryless Sequences for General Losses

  Rafael Frongillo, Andrew Nobel (80):1−28, 2020 PDF BibTeX

• 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 codePDF BibTeX

• Sequential change-point detection in high-dimensional Gaussian graphical models

  Hossein Keshavarz, George Michaildiis, Yves Atchade (82):1−57, 2020 PDF BibTeX

• 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 PDF BibTeX

• Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions

  Christiane Görgen, Manuele Leonelli (84):1−32, 2020 PDF BibTeX

• Effective Ways to Build and Evaluate Individual Survival Distributions

  Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner (85):1−63, 2020 codePDF BibTeX

• Convergence Rate of Optimal Quantization and Application to the Clustering Performance of the Empirical Measure

  Yating Liu, Gilles Pagès (86):1−36, 2020 PDF BibTeX

• 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 codePDF BibTeX

• Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators

  Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi (88):1−54, 2020 codePDF BibTeX

• 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 codePDF BibTeX

• Probabilistic Symmetries and Invariant Neural Networks

  Benjamin Bloem-Reddy, Yee Whye Teh (90):1−61, 2020 PDF BibTeX

• Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

  Ming Yu, Varun Gupta, Mladen Kolar (91):1−51, 2020 PDF BibTeX

• Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients

  Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright, Bin Yu (92):1−72, 2020 PDF BibTeX

• Distributed Kernel Ridge Regression with Communications

  Shao-Bo Lin, Di Wang, Ding-Xuan Zhou (93):1−38, 2020 codePDF BibTeX

• Minimax Nonparametric Parallelism Test

  Xin Xing, Meimei Liu, Ping Ma, Wenxuan Zhong (94):1−47, 2020 codePDF BibTeX

• Cornac: A Comparative Framework for Multimodal Recommender Systems

  Aghiles Salah, Quoc-Tuan Truong, Hady W. Lauw (95):1−5, 2020 codePDF BibTeX

• pyDML: A Python Library for Distance Metric Learning

  Juan Luis Suárez, Salvador García, Francisco Herrera (96):1−7, 2020 codePDF BibTeX

• 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 codePDF BibTeX

• A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings

  Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi (98):1−67, 2020 PDF BibTeX

• Joint Causal Inference from Multiple Contexts

  Joris M. Mooij, Sara Magliacane, Tom Claassen (99):1−108, 2020 codePDF BibTeX

• General Latent Feature Models for Heterogeneous Datasets

  Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani (100):1−49, 2020 codePDF BibTeX

• Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size

  Francois Kamper, Sarel J. Steel, Johan A. du Preez (101):1−42, 2020 PDF BibTeX

• AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)

  Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé (102):1−12, 2020 codePDF BibTeX

• Stochastic Nested Variance Reduction for Nonconvex Optimization

  Dongruo Zhou, Pan Xu, Quanquan Gu (103):1−63, 2020 PDF BibTeX

• 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 codePDF BibTeX

• Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

  Aryan Mokhtari, Hamed Hassani, Amin Karbasi (105):1−49, 2020 PDF BibTeX

• Quadratic Decomposable Submodular Function Minimization: Theory and Practice

  Pan Li, Niao He, Olgica Milenkovic (106):1−49, 2020 codePDF BibTeX

• Change Point Estimation in a Dynamic Stochastic Block Model

  Monika Bhattacharjee, Moulinath Banerjee, George Michailidis (107):1−59, 2020 PDF BibTeX

• ThunderGBM: Fast GBDTs and Random Forests on GPUs

  Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen (108):1−5, 2020 codePDF BibTeX

• Bayesian Model Selection with Graph Structured Sparsity

  Youngseok Kim, Chao Gao (109):1−61, 2020 PDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model

  Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini (112):1−24, 2020 PDF BibTeX

• Prediction regions through Inverse Regression

  Emilie Devijver, Emeline Perthame (113):1−24, 2020 codePDF BibTeX

• 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 PDF BibTeX

• 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 PDF BibTeX

• 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 codePDF BibTeX

• Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models

  Jiahe Lin, George Michailidis (117):1−51, 2020 codePDF BibTeX

• 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 codePDF BibTeX

• Bayesian Closed Surface Fitting Through Tensor Products

  Olivier Binette, Debdeep Pati, David B. Dunson (119):1−26, 2020 PDF BibTeX

• A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning

  Aryan Mokhtari, Alec Koppel, Martin Takac, Alejandro Ribeiro (120):1−51, 2020 PDF BibTeX

• Agnostic Estimation for Phase Retrieval

  Matey Neykov, Zhaoran Wang, Han Liu (121):1−39, 2020 PDF BibTeX

• Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering

  Israel A. Almodóvar-Rivera, Ranjan Maitra (122):1−54, 2020 codePDF BibTeX

• Tensor Regression Networks

  Jean Kossaifi, Zachary C. Lipton, Arinbjorn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar (123):1−21, 2020 PDF BibTeX

• Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination

  Hang Yu, Songwei Wu, Luyin Xin, Justin Dauwels (124):1−54, 2020 codePDF BibTeX

• Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization

  Rad Niazadeh, Tim Roughgarden, Joshua R. Wang (125):1−31, 2020 PDF BibTeX

• Distributed Minimum Error Entropy Algorithms

  Xin Guo, Ting Hu, Qiang Wu (126):1−31, 2020 PDF BibTeX

• 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 codePDF BibTeX

• A Regularization-Based Adaptive Test for High-Dimensional GLMs

  Chong Wu, Gongjun Xu, Xiaotong Shen, Wei Pan (128):1−67, 2020 codePDF BibTeX

• A General System of Differential Equations to Model First-Order Adaptive Algorithms

  Andre Belotto da Silva, Maxime Gazeau (129):1−42, 2020 PDF BibTeX

• 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 codePDF BibTeX

• Convergence of Sparse Variational Inference in Gaussian Processes Regression

  David R. Burt, Carl Edward Rasmussen, Mark van der Wilk (131):1−63, 2020 codePDF BibTeX

• Monte Carlo Gradient Estimation in Machine Learning

  Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih (132):1−62, 2020 codePDF BibTeX

• 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 PDF BibTeX

• Probabilistic Learning on Graphs via Contextual Architectures

  Davide Bacciu, Federico Errica, Alessio Micheli (134):1−39, 2020 codePDF BibTeX

• A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks

  Owen Marschall, Kyunghyun Cho, Cristina Savin (135):1−34, 2020 codePDF BibTeX

• Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions

  Benjamin Fehrman, Benjamin Gess, Arnulf Jentzen (136):1−48, 2020 PDF BibTeX

• Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting

  Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang (137):1−45, 2020 PDF BibTeX

• metric-learn: Metric Learning Algorithms in Python

  William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet (138):1−6, 2020 codePDF BibTeX

• Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks

  Amir R. Asadi, Emmanuel Abbe (139):1−32, 2020 codePDF BibTeX

• 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 codePDF BibTeX

• Importance Sampling Techniques for Policy Optimization

  Alberto Maria Metelli, Matteo Papini, Nico Montali, Marcello Restelli (141):1−75, 2020 codePDF BibTeX

• Nesterov's Acceleration for Approximate Newton

  Haishan Ye, Luo Luo, Zhihua Zhang (142):1−37, 2020 PDF BibTeX

• 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 PDF BibTeX

• Empirical Priors for Prediction in Sparse High-dimensional Linear Regression

  Ryan Martin, Yiqi Tang (144):1−30, 2020 PDF BibTeX

• Orlicz Random Fourier Features

  Linda Chamakh, Emmanuel Gobet, Zoltán Szabó (145):1−37, 2020 PDF BibTeX

• New Insights and Perspectives on the Natural Gradient Method

  James Martens (146):1−76, 2020 PDF BibTeX

• Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

  Junhong Lin, Volkan Cevher (147):1−63, 2020 PDF BibTeX

• Local Causal Network Learning for Finding Pairs of Total and Direct Effects

  Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng, Chunchen Liu (148):1−37, 2020 PDF BibTeX

• Distributionally Ambiguous Optimization for Batch Bayesian Optimization

  Nikitas Rontsis, Michael A. Osborne, Paul J. Goulart (149):1−26, 2020 codePDF BibTeX

• The Kalai-Smorodinsky solution for many-objective Bayesian optimization

  Mickael Binois, Victor Picheny, Patrick Taillandier, Abderrahmane Habbal (150):1−42, 2020 codePDF BibTeX

• Robust Reinforcement Learning with Bayesian Optimisation and Quadrature

  Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne, Shimon Whiteson (151):1−31, 2020 PDF BibTeX

• Dual Iterative Hard Thresholding

  Xiao-Tong Yuan, Bo Liu, Lezi Wang, Qingshan Liu, Dimitris N. Metaxas (152):1−50, 2020 PDF BibTeX

• Spectral Algorithms for Community Detection in Directed Networks

  Zhe Wang, Yingbin Liang, Pengsheng Ji (153):1−45, 2020 PDF BibTeX

• Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality

  Miaoyan Wang, Lexin Li (154):1−38, 2020 PDF BibTeX

• Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise

  Andrei Kulunchakov, Julien Mairal (155):1−52, 2020 PDF BibTeX

• Asymptotic Consistency of $\alpha$-{R}\'enyi-Approximate Posteriors

  Prateek Jaiswal, Vinayak Rao, Harsha Honnappa (156):1−42, 2020 PDF BibTeX

• Streamlined Variational Inference with Higher Level Random Effects

  Tui H. Nolan, Marianne Menictas, Matt P. Wand (157):1−62, 2020 codePDF BibTeX

• Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion

  Jiaying Gu, Qing Zhou (158):1−31, 2020 PDF BibTeX

• Generating Weighted MAX-2-SAT Instances with Frustrated Loops: an RBM Case Study

  Yan Ru Pei, Haik Manukian, Massimiliano Di Ventra (159):1−55, 2020 codePDF BibTeX

• Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective

  Chao Gao, Yuan Yao, Weizhi Zhu (160):1−48, 2020 codePDF BibTeX

• apricot: Submodular selection for data summarization in Python

  Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble (161):1−6, 2020 codePDF BibTeX

• Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

  Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski (162):1−54, 2020 PDF BibTeX

• Trust-Region Variational Inference with Gaussian Mixture Models

  Oleg Arenz, Mingjun Zhong, Gerhard Neumann (163):1−60, 2020 codePDF BibTeX

• Cramer-Wold Auto-Encoder

  Szymon Knop, Przemysław Spurek, Jacek Tabor, Igor Podolak, Marcin Mazur, Stanisław Jastrzębski (164):1−28, 2020 codePDF BibTeX

• Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group

  Yuexiang Zhai, Zitong Yang, Zhenyu Liao, John Wright, Yi Ma (165):1−68, 2020 PDF BibTeX

• High Dimensional Forecasting via Interpretable Vector Autoregression

  William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson (166):1−52, 2020 codePDF BibTeX

• Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes

  Nathan Kallus, Masatoshi Uehara (167):1−63, 2020 codePDF BibTeX

• Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels

  Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber (168):1−58, 2020 PDF BibTeX

• The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization

  Dmitry Kobak, Jonathan Lomond, Benoit Sanchez (169):1−16, 2020 codePDF BibTeX

• Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior

  William Hoiles, Vikram Krishnamurthy, Kunal Pattanayak (170):1−39, 2020 PDF BibTeX

• Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success

  Lucas Mentch, Siyu Zhou (171):1−36, 2020 PDF BibTeX

• Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise

  Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara (172):1−29, 2020 PDF BibTeX

• Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes

  Emily C. Hector, Peter X.-K. Song (173):1−35, 2020 PDF BibTeX

• Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality

  Ryumei Nakada, Masaaki Imaizumi (174):1−38, 2020 PDF BibTeX

• Wide Neural Networks with Bottlenecks are Deep Gaussian Processes

  Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle (175):1−66, 2020 codePDF BibTeX

• Breaking the Curse of Nonregularity with Subagging --- Inference of the Mean Outcome under Optimal Treatment Regimes

  Chengchun Shi, Wenbin Lu, Rui Song (176):1−67, 2020 PDF BibTeX

• Optimal Estimation of Sparse Topic Models

  Xin Bing, Florentina Bunea, Marten Wegkamp (177):1−45, 2020 PDF BibTeX

• Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

  Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson (178):1−51, 2020 codePDF BibTeX

• Variational Inference for Computational Imaging Inverse Problems

  Francesco Tonolini, Jack Radford, Alex Turpin, Daniele Faccio, Roderick Murray-Smith (179):1−46, 2020 PDF BibTeX

• Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction

  Boyue Li, Shicong Cen, Yuxin Chen, Yuejie Chi (180):1−51, 2020 codePDF BibTeX

• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

  Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone (181):1−50, 2020 PDF BibTeX

• Distributed High-dimensional Regression Under a Quantile Loss Function

  Xi Chen, Weidong Liu, Xiaojun Mao, Zhuoyi Yang (182):1−43, 2020 PDF BibTeX

• Near-optimal Individualized Treatment Recommendations

  Haomiao Meng, Ying-Qi Zhao, Haoda Fu, Xingye Qiao (183):1−28, 2020 PDF BibTeX

• Topology of Deep Neural Networks

  Gregory Naitzat, Andrey Zhitnikov, Lek-Heng Lim (184):1−40, 2020 codePDF BibTeX

• Scikit-network: Graph Analysis in Python

  Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier (185):1−6, 2020 codePDF BibTeX

• Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods

  Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M Stuart (186):1−55, 2020 PDF BibTeX

• Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs

  Rui Tuo, Wenjia Wang (187):1−38, 2020 PDF BibTeX

• Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models

  Andrea Rotnitzky, Ezequiel Smucler (188):1−86, 2020 PDF BibTeX

• Beyond Trees: Classification with Sparse Pairwise Dependencies

  Yaniv Tenzer, Amit Moscovich, Mary Frances Dorn, Boaz Nadler, Clifford Spiegelman (189):1−33, 2020 PDF BibTeX

• A Unified q-Memorization Framework for Asynchronous Stochastic Optimization

  Bin Gu, Wenhan Xian, Zhouyuan Huo, Cheng Deng, Heng Huang (190):1−53, 2020 PDF BibTeX

• Adaptive Smoothing for Path Integral Control

  Dominik Thalmeier, Hilbert J. Kappen, Simone Totaro, Vicenç Gómez (191):1−37, 2020 codePDF BibTeX

• Semi-parametric Learning of Structured Temporal Point Processes

  Ganggang Xu, Ming Wang, Jiangze Bian, Hui Huang, Timothy R. Burch, Sandro C. Andrade, Jingfei Zhang, Yongtao Guan (192):1−39, 2020 PDF BibTeX

• Learning and Interpreting Multi-Multi-Instance Learning Networks

  Alessandro Tibo, Manfred Jaeger, Paolo Frasconi (193):1−60, 2020 PDF BibTeX

• Contextual Explanation Networks

  Maruan Al-Shedivat, Avinava Dubey, Eric Xing (194):1−44, 2020 codePDF BibTeX

• Conic Optimization for Quadratic Regression Under Sparse Noise

  Igor Molybog, Ramtin Madani, Javad Lavaei (195):1−36, 2020 codePDF BibTeX

• Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning

  Lucas Lehnert, Michael L. Littman (196):1−53, 2020 PDF BibTeX

• A determinantal point process for column subset selection

  Ayoub Belhadji, Rémi Bardenet, Pierre Chainais (197):1−62, 2020 PDF BibTeX

• Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach

  Haoran Wang, Thaleia Zariphopoulou, Xun Yu Zhou (198):1−34, 2020 PDF BibTeX

• Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms

  Yazhen Wang, Shang Wu (199):1−103, 2020 PDF BibTeX

• Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy

  Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu (200):1−39, 2020 PDF BibTeX

• Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models

  Reza Mohammadi, Matthew Pratola, Maurits Kaptein (201):1−26, 2020 PDF BibTeX

• A Numerical Measure of the Instability of Mapper-Type Algorithms

  Francisco Belchi, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan (202):1−45, 2020 codePDF BibTeX

• Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance

  Dalit Engelhardt (203):1−30, 2020 PDF BibTeX

• Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data

  Martin Slawski, Emanuel Ben-David, Ping Li (204):1−42, 2020 PDF BibTeX

• Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms

  Simon Fischer, Ingo Steinwart (205):1−38, 2020 PDF BibTeX

• On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond

  Xiao-Tong Yuan, Ping Li (206):1−51, 2020 PDF BibTeX

• Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging

  Baihua He, Yanyan Liu, Yuanshan Wu, Guosheng Yin, Xingqiu Zhao (207):1−37, 2020 PDF BibTeX

• Learning Data-adaptive Non-parametric Kernels

  Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li (208):1−39, 2020 PDF BibTeX

• A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

  Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem (209):1−62, 2020 codePDF BibTeX

• ProtoAttend: Attention-Based Prototypical Learning

  Sercan O. Arik, Tomas Pfister (210):1−35, 2020 PDF BibTeX

• Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images

  Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang (211):1−21, 2020 codePDF BibTeX

• scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn

  Sebastian Pölsterl (212):1−6, 2020 codePDF BibTeX

• Multiclass Anomaly Detector: the CS++ Support Vector Machine

  Alistair Shilton, Sutharshan Rajasegarar, Marimuthu Palaniswami (213):1−39, 2020 PDF BibTeX

• Provable Convex Co-clustering of Tensors

  Eric C. Chi, Brian J. Gaines, Will Wei Sun, Hua Zhou, Jian Yang (214):1−58, 2020 PDF BibTeX

• Mining Topological Structure in Graphs through Forest Representations

  Robin Vandaele, Yvan Saeys, Tijl De Bie (215):1−68, 2020 PDF BibTeX

• Dynamic Assortment Optimization with Changing Contextual Information

  Xi Chen, Yining Wang, Yuan Zhou (216):1−44, 2020 PDF BibTeX

• On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach

  Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique Del Castillo (217):1−41, 2020 codePDF BibTeX

• Spectral bandits

  Tomáš Kocák, Rémi Munos, Branislav Kveton, Shipra Agrawal, Michal Valko (218):1−44, 2020 PDF BibTeX

• AdaGrad stepsizes: Sharp convergence over nonconvex landscapes

  Rachel Ward, Xiaoxia Wu, Leon Bottou (219):1−30, 2020 PDF BibTeX

• Diffeomorphic Learning

  Laurent Younes (220):1−28, 2020 PDF BibTeX

• Learning Sums of Independent Random Variables with Sparse Collective Support

  Anindya De, Philip M. Long, Rocco A. Servedio (221):1−79, 2020 PDF BibTeX

• Theory of Curriculum Learning, with Convex Loss Functions

  Daphna Weinshall, Dan Amir (222):1−19, 2020 PDF BibTeX

• Geomstats: A Python Package for Riemannian Geometry in Machine Learning

  Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec (223):1−9, 2020 codePDF BibTeX

• Ultra-High Dimensional Single-Index Quantile Regression

  Yuankun Zhang, Heng Lian, Yan Yu (224):1−25, 2020 PDF BibTeX

• Recovery of a Mixture of Gaussians by Sum-of-Norms Clustering

  Tao Jiang, Stephen Vavasis, Chen Wen Zhai (225):1−16, 2020 PDF BibTeX

• A Sparse Semismooth Newton Based Proximal Majorization-Minimization Algorithm for Nonconvex Square-Root-Loss Regression Problems

  Peipei Tang, Chengjing Wang, Defeng Sun, Kim-Chuan Toh (226):1−38, 2020 PDF BibTeX

• Significance Tests for Neural Networks

  Enguerrand Horel, Kay Giesecke (227):1−29, 2020 PDF BibTeX

• Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications

  Frederik Heber, Žofia Trst’anová, Benedict Leimkuhler (228):1−33, 2020 codePDF BibTeX

• Nonparametric graphical model for counts

  Arkaprava Roy, David B Dunson (229):1−21, 2020 codePDF BibTeX

• Stable Regression: On the Power of Optimization over Randomization

  Dimitris Bertsimas, Ivan Paskov (230):1−25, 2020 PDF BibTeX

• Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion

  Dimitris Bertsimas, Michael Lingzhi Li (231):1−43, 2020 PDF BibTeX

• Spectral Deconfounding via Perturbed Sparse Linear Models

  Domagoj Ćevid, Peter Bühlmann, Nicolai Meinshausen (232):1−41, 2020 PDF BibTeX

• Robust high dimensional learning for Lipschitz and convex losses

  Chinot Geoffrey, Lecué Guillaume, Lerasle Matthieu (233):1−47, 2020 PDF BibTeX

• Dual Extrapolation for Sparse GLMs

  Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon (234):1−33, 2020 codePDF BibTeX

• Convex Programming for Estimation in Nonlinear Recurrent Models

  Sohail Bahmani, Justin Romberg (235):1−20, 2020 PDF BibTeX

• Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information

  Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur (236):1−30, 2020 PDF BibTeX

• The Error-Feedback framework: SGD with Delayed Gradients

  Sebastian U. Stich, Sai Praneeth Karimireddy (237):1−36, 2020 PDF BibTeX

• algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD

  Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui (238):1−6, 2020 codePDF BibTeX

• Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection

  Joonas Hämäläinen, Alisson S. C. Alencar, Tommi Kärkkäinen, César L. C. Mattos, Amauri H. Souza Júnior, João P. P. Gomes (239):1−29, 2020 PDF BibTeX

• Risk Bounds for Reservoir Computing

  Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega (240):1−61, 2020 PDF BibTeX

• Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables

  Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen (241):1−31, 2020 PDF BibTeX

• Fair Data Adaptation with Quantile Preservation

  Drago Plečko, Nicolai Meinshausen (242):1−44, 2020 codePDF BibTeX

• Best Practices for Scientific Research on Neural Architecture Search

  Marius Lindauer, Frank Hutter (243):1−18, 2020 codePDF BibTeX

• Rank-based Lasso - efficient methods for high-dimensional robust model selection

  Wojciech Rejchel, Małgorzata Bogdan (244):1−47, 2020 PDF BibTeX

• A Group-Theoretic Framework for Data Augmentation

  Shuxiao Chen, Edgar Dobriban, Jane H. Lee (245):1−71, 2020 codePDF BibTeX

• On Efficient Adjustment in Causal Graphs

  Janine Witte, Leonard Henckel, Marloes H. Maathuis, Vanessa Didelez (246):1−45, 2020 PDF BibTeX

• Adaptive Rates for Total Variation Image Denoising

  Francesco Ortelli, Sara van de Geer (247):1−38, 2020 codePDF BibTeX

• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

  Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau (248):1−43, 2020 codePDF BibTeX

• Learning Mixed Latent Tree Models

  Can Zhou, Xiaofei Wang, Jianhua Guo (249):1−35, 2020 PDF BibTeX

• High-dimensional quantile tensor regression

  Wenqi Lu, Zhongyi Zhu, Heng Lian (250):1−31, 2020 PDF BibTeX

• Online matrix factorization for Markovian data and applications to Network Dictionary Learning

  Hanbaek Lyu, Deanna Needell, Laura Balzano (251):1−49, 2020 codePDF BibTeX

• Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra

  Vardan Papyan (252):1−64, 2020 PDF BibTeX