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Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing.


Latest papers

Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra
Vardan Papyan, 2020.
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Online matrix factorization for Markovian data and applications to Network Dictionary Learning
Hanbaek Lyu, Deanna Needell, Laura Balzano, 2020.
[abs][pdf][bib]      [code]

High-dimensional quantile tensor regression
Wenqi Lu, Zhongyi Zhu, Heng Lian, 2020.
[abs][pdf][bib]

Learning Mixed Latent Tree Models
Can Zhou, Xiaofei Wang, Jianhua Guo, 2020.
[abs][pdf][bib]

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, 2020.
[abs][pdf][bib]      [code]

Adaptive Rates for Total Variation Image Denoising
Francesco Ortelli, Sara van de Geer, 2020.
[abs][pdf][bib]      [code]

On Efficient Adjustment in Causal Graphs
Janine Witte, Leonard Henckel, Marloes H. Maathuis, Vanessa Didelez, 2020.
[abs][pdf][bib]

A Group-Theoretic Framework for Data Augmentation
Shuxiao Chen, Edgar Dobriban, Jane H. Lee, 2020.
[abs][pdf][bib]      [code]

Rank-based Lasso - efficient methods for high-dimensional robust model selection
Wojciech Rejchel, Małgorzata Bogdan, 2020.
[abs][pdf][bib]

Best Practices for Scientific Research on Neural Architecture Search
Marius Lindauer, Frank Hutter, 2020.
[abs][pdf][bib]      [code]

Fair Data Adaptation with Quantile Preservation
Drago Plečko, Nicolai Meinshausen, 2020.
[abs][pdf][bib]      [code]

Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables
Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen, 2020.
[abs][pdf][bib]

Risk Bounds for Reservoir Computing
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, 2020.
[abs][pdf][bib]

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, 2020.
[abs][pdf][bib]

algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

The Error-Feedback framework: SGD with Delayed Gradients
Sebastian U. Stich, Sai Praneeth Karimireddy, 2020.
[abs][pdf][bib]

Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information
Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur, 2020.
[abs][pdf][bib]

Convex Programming for Estimation in Nonlinear Recurrent Models
Sohail Bahmani, Justin Romberg, 2020.
[abs][pdf][bib]

Dual Extrapolation for Sparse GLMs
Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon, 2020.
[abs][pdf][bib]      [code]

Robust high dimensional learning for Lipschitz and convex losses
Chinot Geoffrey, Lecué Guillaume, Lerasle Matthieu, 2020.
[abs][pdf][bib]

Spectral Deconfounding via Perturbed Sparse Linear Models
Domagoj Ćevid, Peter Bühlmann, Nicolai Meinshausen, 2020.
[abs][pdf][bib]

Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion
Dimitris Bertsimas, Michael Lingzhi Li, 2020.
[abs][pdf][bib]

Stable Regression: On the Power of Optimization over Randomization
Dimitris Bertsimas, Ivan Paskov, 2020.
[abs][pdf][bib]

Nonparametric graphical model for counts
Arkaprava Roy, David B Dunson, 2020.
[abs][pdf][bib]      [code]

Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications
Frederik Heber, Žofia Trst’anová, Benedict Leimkuhler, 2020.
[abs][pdf][bib]      [code]

Significance Tests for Neural Networks
Enguerrand Horel, Kay Giesecke, 2020.
[abs][pdf][bib]

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, 2020.
[abs][pdf][bib]

Recovery of a Mixture of Gaussians by Sum-of-Norms Clustering
Tao Jiang, Stephen Vavasis, Chen Wen Zhai, 2020.
[abs][pdf][bib]

Ultra-High Dimensional Single-Index Quantile Regression
Yuankun Zhang, Heng Lian, Yan Yu, 2020.
[abs][pdf][bib]

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, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Theory of Curriculum Learning, with Convex Loss Functions
Daphna Weinshall, Dan Amir, 2020.
[abs][pdf][bib]

Learning Sums of Independent Random Variables with Sparse Collective Support
Anindya De, Philip M. Long, Rocco A. Servedio, 2020.
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Diffeomorphic Learning
Laurent Younes, 2020.
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AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
Rachel Ward, Xiaoxia Wu, Leon Bottou, 2020.
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Spectral bandits
Tomáš Kocák, Rémi Munos, Branislav Kveton, Shipra Agrawal, Michal Valko, 2020.
[abs][pdf][bib]

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, 2020.
[abs][pdf][bib]      [code]

Dynamic Assortment Optimization with Changing Contextual Information
Xi Chen, Yining Wang, Yuan Zhou, 2020.
[abs][pdf][bib]

Mining Topological Structure in Graphs through Forest Representations
Robin Vandaele, Yvan Saeys, Tijl De Bie, 2020.
[abs][pdf][bib]

Provable Convex Co-clustering of Tensors
Eric C. Chi, Brian J. Gaines, Will Wei Sun, Hua Zhou, Jian Yang, 2020.
[abs][pdf][bib]

Multiclass Anomaly Detector: the CS++ Support Vector Machine
Alistair Shilton, Sutharshan Rajasegarar, Marimuthu Palaniswami, 2020.
[abs][pdf][bib]

scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn
Sebastian Pölsterl, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images
Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang, 2020.
[abs][pdf][bib]      [code]

ProtoAttend: Attention-Based Prototypical Learning
Sercan O. Arik, Tomas Pfister, 2020.
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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, 2020.
[abs][pdf][bib]      [code]

Learning Data-adaptive Non-parametric Kernels
Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li, 2020.
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Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging
Baihua He, Yanyan Liu, Yuanshan Wu, Guosheng Yin, Xingqiu Zhao, 2020.
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On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond
Xiao-Tong Yuan, Ping Li, 2020.
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Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms
Simon Fischer, Ingo Steinwart, 2020.
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Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data
Martin Slawski, Emanuel Ben-David, Ping Li, 2020.
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Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance
Dalit Engelhardt, 2020.
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A Numerical Measure of the Instability of Mapper-Type Algorithms
Francisco Belchi, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan, 2020.
[abs][pdf][bib]      [code]

Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models
Reza Mohammadi, Matthew Pratola, Maurits Kaptein, 2020.
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Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu, 2020.
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Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms
Yazhen Wang, Shang Wu, 2020.
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Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach
Haoran Wang, Thaleia Zariphopoulou, Xun Yu Zhou, 2020.
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A determinantal point process for column subset selection
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais, 2020.
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Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning
Lucas Lehnert, Michael L. Littman, 2020.
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Conic Optimization for Quadratic Regression Under Sparse Noise
Igor Molybog, Ramtin Madani, Javad Lavaei, 2020.
[abs][pdf][bib]      [code]

Contextual Explanation Networks
Maruan Al-Shedivat, Avinava Dubey, Eric Xing, 2020.
[abs][pdf][bib]      [code]

Learning and Interpreting Multi-Multi-Instance Learning Networks
Alessandro Tibo, Manfred Jaeger, Paolo Frasconi, 2020.
[abs][pdf][bib]

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, 2020.
[abs][pdf][bib]

Adaptive Smoothing for Path Integral Control
Dominik Thalmeier, Hilbert J. Kappen, Simone Totaro, Vicenç Gómez, 2020.
[abs][pdf][bib]      [code]

A Unified q-Memorization Framework for Asynchronous Stochastic Optimization
Bin Gu, Wenhan Xian, Zhouyuan Huo, Cheng Deng, Heng Huang, 2020.
[abs][pdf][bib]

Beyond Trees: Classification with Sparse Pairwise Dependencies
Yaniv Tenzer, Amit Moscovich, Mary Frances Dorn, Boaz Nadler, Clifford Spiegelman, 2020.
[abs][pdf][bib]

Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models
Andrea Rotnitzky, Ezequiel Smucler, 2020.
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Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs
Rui Tuo, Wenjia Wang, 2020.
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Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods
Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M Stuart, 2020.
[abs][pdf][bib]

Scikit-network: Graph Analysis in Python
Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Topology of Deep Neural Networks
Gregory Naitzat, Andrey Zhitnikov, Lek-Heng Lim, 2020.
[abs][pdf][bib]      [code]

Near-optimal Individualized Treatment Recommendations
Haomiao Meng, Ying-Qi Zhao, Haoda Fu, Xingye Qiao, 2020.
[abs][pdf][bib]

Distributed High-dimensional Regression Under a Quantile Loss Function
Xi Chen, Weidong Liu, Xiaojun Mao, Zhuoyi Yang, 2020.
[abs][pdf][bib]

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone, 2020.
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Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
Boyue Li, Shicong Cen, Yuxin Chen, Yuejie Chi, 2020.
[abs][pdf][bib]      [code]

Variational Inference for Computational Imaging Inverse Problems
Francesco Tonolini, Jack Radford, Alex Turpin, Daniele Faccio, Roderick Murray-Smith, 2020.
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Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson, 2020.
[abs][pdf][bib]      [code]

Optimal Estimation of Sparse Topic Models
Xin Bing, Florentina Bunea, Marten Wegkamp, 2020.
[abs][pdf][bib]

Breaking the Curse of Nonregularity with Subagging --- Inference of the Mean Outcome under Optimal Treatment Regimes
Chengchun Shi, Wenbin Lu, Rui Song, 2020.
[abs][pdf][bib]

Wide Neural Networks with Bottlenecks are Deep Gaussian Processes
Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle, 2020.
[abs][pdf][bib]      [code]

Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality
Ryumei Nakada, Masaaki Imaizumi, 2020.
[abs][pdf][bib]

Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes
Emily C. Hector, Peter X.-K. Song, 2020.
[abs][pdf][bib]

Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise
Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara, 2020.
[abs][pdf][bib]

Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success
Lucas Mentch, Siyu Zhou, 2020.
[abs][pdf][bib]

Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior
William Hoiles, Vikram Krishnamurthy, Kunal Pattanayak, 2020.
[abs][pdf][bib]

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, 2020.
[abs][pdf][bib]      [code]

Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels
Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber, 2020.
[abs][pdf][bib]

Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes
Nathan Kallus, Masatoshi Uehara, 2020.
[abs][pdf][bib]      [code]

High Dimensional Forecasting via Interpretable Vector Autoregression
William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson, 2020.
[abs][pdf][bib]      [code]

Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group
Yuexiang Zhai, Zitong Yang, Zhenyu Liao, John Wright, Yi Ma, 2020.
[abs][pdf][bib]

Cramer-Wold Auto-Encoder
Szymon Knop, Przemysław Spurek, Jacek Tabor, Igor Podolak, Marcin Mazur, Stanisław Jastrzębski, 2020.
[abs][pdf][bib]      [code]

Trust-Region Variational Inference with Gaussian Mixture Models
Oleg Arenz, Mingjun Zhong, Gerhard Neumann, 2020.
[abs][pdf][bib]      [code]

Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski, 2020.
[abs][pdf][bib]

apricot: Submodular selection for data summarization in Python
Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective
Chao Gao, Yuan Yao, Weizhi Zhu, 2020.
[abs][pdf][bib]      [code]

Generating Weighted MAX-2-SAT Instances with Frustrated Loops: an RBM Case Study
Yan Ru Pei, Haik Manukian, Massimiliano Di Ventra, 2020.
[abs][pdf][bib]      [code]

Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion
Jiaying Gu, Qing Zhou, 2020.
[abs][pdf][bib]

Streamlined Variational Inference with Higher Level Random Effects
Tui H. Nolan, Marianne Menictas, Matt P. Wand, 2020.
[abs][pdf][bib]      [code]

Asymptotic Consistency of $\alpha$-{R}\'enyi-Approximate Posteriors
Prateek Jaiswal, Vinayak Rao, Harsha Honnappa, 2020.
[abs][pdf][bib]

Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise
Andrei Kulunchakov, Julien Mairal, 2020.
[abs][pdf][bib]

Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality
Miaoyan Wang, Lexin Li, 2020.
[abs][pdf][bib]

Spectral Algorithms for Community Detection in Directed Networks
Zhe Wang, Yingbin Liang, Pengsheng Ji, 2020.
[abs][pdf][bib]

Dual Iterative Hard Thresholding
Xiao-Tong Yuan, Bo Liu, Lezi Wang, Qingshan Liu, Dimitris N. Metaxas, 2020.
[abs][pdf][bib]

Robust Reinforcement Learning with Bayesian Optimisation and Quadrature
Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne, Shimon Whiteson, 2020.
[abs][pdf][bib]

The Kalai-Smorodinsky solution for many-objective Bayesian optimization
Mickael Binois, Victor Picheny, Patrick Taillandier, Abderrahmane Habbal, 2020.
[abs][pdf][bib]      [code]

Distributionally Ambiguous Optimization for Batch Bayesian Optimization
Nikitas Rontsis, Michael A. Osborne, Paul J. Goulart, 2020.
[abs][pdf][bib]      [code]

Local Causal Network Learning for Finding Pairs of Total and Direct Effects
Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng, Chunchen Liu, 2020.
[abs][pdf][bib]

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
Junhong Lin, Volkan Cevher, 2020.
[abs][pdf][bib]

New Insights and Perspectives on the Natural Gradient Method
James Martens, 2020.
[abs][pdf][bib]

Orlicz Random Fourier Features
Linda Chamakh, Emmanuel Gobet, Zoltán Szabó, 2020.
[abs][pdf][bib]

Empirical Priors for Prediction in Sparse High-dimensional Linear Regression
Ryan Martin, Yiqi Tang, 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, 2020.
[abs][pdf][bib]

Nesterov's Acceleration for Approximate Newton
Haishan Ye, Luo Luo, Zhihua Zhang, 2020.
[abs][pdf][bib]

Importance Sampling Techniques for Policy Optimization
Alberto Maria Metelli, Matteo Papini, Nico Montali, Marcello Restelli, 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, 2020.
[abs][pdf][bib]      [code]

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks
Amir R. Asadi, Emmanuel Abbe, 2020.
[abs][pdf][bib]      [code]

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

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang, 2020.
[abs][pdf][bib]

Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions
Benjamin Fehrman, Benjamin Gess, Arnulf Jentzen, 2020.
[abs][pdf][bib]

A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks
Owen Marschall, Kyunghyun Cho, Cristina Savin, 2020.
[abs][pdf][bib]      [code]

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

Monte Carlo Gradient Estimation in Machine Learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih, 2020.
[abs][pdf][bib]      [code]

Convergence of Sparse Variational Inference in Gaussian Processes Regression
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk, 2020.
[abs][pdf][bib]      [code]

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, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

A General System of Differential Equations to Model First-Order Adaptive Algorithms
Andre Belotto da Silva, Maxime Gazeau, 2020.
[abs][pdf][bib]

A Regularization-Based Adaptive Test for High-Dimensional GLMs
Chong Wu, Gongjun Xu, Xiaotong Shen, Wei Pan, 2020.
[abs][pdf][bib]      [code]

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, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Distributed Minimum Error Entropy Algorithms
Xin Guo, Ting Hu, Qiang Wu, 2020.
[abs][pdf][bib]

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh, Tim Roughgarden, Joshua R. Wang, 2020.
[abs][pdf][bib]

Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination
Hang Yu, Songwei Wu, Luyin Xin, Justin Dauwels, 2020.
[abs][pdf][bib]      [code]

Tensor Regression Networks
Jean Kossaifi, Zachary C. Lipton, Arinbjorn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar, 2020.
[abs][pdf][bib]

Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering
Israel A. Almodóvar-Rivera, Ranjan Maitra, 2020.
[abs][pdf][bib]      [code]

Agnostic Estimation for Phase Retrieval
Matey Neykov, Zhaoran Wang, Han Liu, 2020.
[abs][pdf][bib]

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
Aryan Mokhtari, Alec Koppel, Martin Takac, Alejandro Ribeiro, 2020.
[abs][pdf][bib]

Bayesian Closed Surface Fitting Through Tensor Products
Olivier Binette, Debdeep Pati, David B. Dunson, 2020.
[abs][pdf][bib]

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, 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, 2020.
[abs][pdf][bib]      [code]

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, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space
Elisabeth Gassiat, Sylvain Le Corff, Luc Lehéricy, 2020.
[abs][pdf][bib]

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

Prediction regions through Inverse Regression
Emilie Devijver, Emeline Perthame, 2020.
[abs][pdf][bib]      [code]

High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model
Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini, 2020.
[abs][pdf][bib]

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, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

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

Bayesian Model Selection with Graph Structured Sparsity
Youngseok Kim, Chao Gao, 2020.
[abs][pdf][bib]

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

Change Point Estimation in a Dynamic Stochastic Block Model
Monika Bhattacharjee, Moulinath Banerjee, George Michailidis, 2020.
[abs][pdf][bib]

Quadratic Decomposable Submodular Function Minimization: Theory and Practice
Pan Li, Niao He, Olgica Milenkovic, 2020.
[abs][pdf][bib]      [code]

Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
Aryan Mokhtari, Hamed Hassani, Amin Karbasi, 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, 2020.
[abs][pdf][bib]      [code]

Stochastic Nested Variance Reduction for Nonconvex Optimization
Dongruo Zhou, Pan Xu, Quanquan Gu, 2020.
[abs][pdf][bib]

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

Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size
Francois Kamper, Sarel J. Steel, Johan A. du Preez, 2020.
[abs][pdf][bib]

General Latent Feature Models for Heterogeneous Datasets
Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani, 2020.
[abs][pdf][bib]      [code]

Joint Causal Inference from Multiple Contexts
Joris M. Mooij, Sara Magliacane, Tom Claassen, 2020.
[abs][pdf][bib]      [code]

A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings
Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi, 2020.
[abs][pdf][bib]

Loss Control with Rank-one Covariance Estimate for Short-term Portfolio Optimization
Zhao-Rong Lai, Liming Tan, Xiaotian Wu, Liangda Fang, 2020.
[abs][pdf][bib]      [code]

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

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

Minimax Nonparametric Parallelism Test
Xin Xing, Meimei Liu, Ping Ma, Wenxuan Zhong, 2020.
[abs][pdf][bib]      [code]

Distributed Kernel Ridge Regression with Communications
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou, 2020.
[abs][pdf][bib]      [code]

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

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching
Ming Yu, Varun Gupta, Mladen Kolar, 2020.
[abs][pdf][bib]

Probabilistic Symmetries and Invariant Neural Networks
Benjamin Bloem-Reddy, Yee Whye Teh, 2020.
[abs][pdf][bib]

Causal Discovery from Heterogeneous/Nonstationary Data
Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf, 2020.
[abs][pdf][bib]      [code]

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

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

Convergence Rate of Optimal Quantization and Application to the Clustering Performance of the Empirical Measure
Yating Liu, Gilles Pagès, 2020.
[abs][pdf][bib]

Effective Ways to Build and Evaluate Individual Survival Distributions
Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner, 2020.
[abs][pdf][bib]      [code]

Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions
Christiane Görgen, Manuele Leonelli, 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, 2020.
[abs][pdf][bib]

Sequential change-point detection in high-dimensional Gaussian graphical models
Hossein Keshavarz, George Michaildiis, Yves Atchade, 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, 2020.
[abs][pdf][bib]      [code]

Memoryless Sequences for General Losses
Rafael Frongillo, Andrew Nobel, 2020.
[abs][pdf][bib]

Quantile Graphical Models: a Bayesian Approach
Nilabja Guha, Veera Baladandayuthapani, Bani K. Mallick, 2020.
[abs][pdf][bib]

Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms
Malte Probst, Franz Rothlauf, 2020.
[abs][pdf][bib]      [code]

Multi-Player Bandits: The Adversarial Case
Pragnya Alatur, Kfir Y. Levy, Andreas Krause, 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, 2020.
[abs][pdf][bib]

Identifiability of Additive Noise Models Using Conditional Variances
Gunwoong Park, 2020.
[abs][pdf][bib]

High-dimensional Gaussian graphical models on network-linked data
Tianxi Li, Cheng Qian, Elizaveta Levina, Ji Zhu, 2020.
[abs][pdf][bib]      [code]

Scalable Approximate MCMC Algorithms for the Horseshoe Prior
James Johndrow, Paulo Orenstein, Anirban Bhattacharya, 2020.
[abs][pdf][bib]

(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, 2020.
[abs][pdf][bib]      [code]

Estimation of a Low-rank Topic-Based Model for Information Cascades
Ming Yu, Varun Gupta, Mladen Kolar, 2020.
[abs][pdf][bib]      [code]

Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart, 2020.
[abs][pdf][bib]

Union of Low-Rank Tensor Spaces: Clustering and Completion
Morteza Ashraphijuo, Xiaodong Wang, 2020.
[abs][pdf][bib]

On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
Xi Chen, Simon S. Du, Xin T. Tong, 2020.
[abs][pdf][bib]

The weight function in the subtree kernel is decisive
Romain Azaïs, Florian Ingels, 2020.
[abs][pdf][bib]      [code]

WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions
Edgar Dobriban, Yue Sheng, 2020.
[abs][pdf][bib]      [code]

Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients
Yu Liu, Kris De Brabanter, 2020.
[abs][pdf][bib]

Community-Based Group Graphical Lasso
Eugen Pircalabelu, Gerda Claeskens, 2020.
[abs][pdf][bib]

Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning
Yu Wang, Siqi Wu, Bin Yu, 2020.
[abs][pdf][bib]

Generalized Optimal Matching Methods for Causal Inference
Nathan Kallus, 2020.
[abs][pdf][bib]

Multiparameter Persistence Landscapes
Oliver Vipond, 2020.
[abs][pdf][bib]

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, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis
Salar Fattahi, Somayeh Sojoudi, 2020.
[abs][pdf][bib]

Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions
Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis, 2020.
[abs][pdf][bib]

Self-paced Multi-view Co-training
Fan Ma, Deyu Meng, Xuanyi Dong, Yi Yang, 2020.
[abs][pdf][bib]      [code]

Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes
Peter D. Grünwald, Nishant A. Mehta, 2020.
[abs][pdf][bib]

Conjugate Gradients for Kernel Machines
Simon Bartels, Philipp Hennig, 2020.
[abs][pdf][bib]      [code]

GraKeL: A Graph Kernel Library in Python
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

High-Dimensional Inference for Cluster-Based Graphical Models
Carson Eisenach, Florentina Bunea, Yang Ning, Claudiu Dinicu, 2020.
[abs][pdf][bib]

Expected Policy Gradients for Reinforcement Learning
Kamil Ciosek, Shimon Whiteson, 2020.
[abs][pdf][bib]

Learning Causal Networks via Additive Faithfulness
Kuang-Yao Lee, Tianqi Liu, Bing Li, Hongyu Zhao, 2020.
[abs][pdf][bib]

Sparse and low-rank multivariate Hawkes processes
Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, Jean-Francois Muzy, 2020.
[abs][pdf][bib]      [code]

Ensemble Learning for Relational Data
Hoda Eldardiry, Jennifer Neville, Ryan A. Rossi, 2020.
[abs][pdf][bib]

Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory
Thomas Ricatte, Rémi Gilleron, Marc Tommasi, 2020.
[abs][pdf][bib]

Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement
Wouter Kool, Herke van Hoof, Max Welling, 2020.
[abs][pdf][bib]      [code]

pyts: A Python Package for Time Series Classification
Johann Faouzi, Hicham Janati, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

A Convex Parametrization of a New Class of Universal Kernel Functions
Brendon K. Colbert, Matthew M. Peet, 2020.
[abs][pdf][bib]

Dynamical Systems as Temporal Feature Spaces
Peter Tino, 2020.
[abs][pdf][bib]

Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan, 2020.
[abs][pdf][bib]

Branch and Bound for Piecewise Linear Neural Network Verification
Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H.S. Torr, Pushmeet Kohli, M. Pawan Kumar, 2020.
[abs][pdf][bib]

Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables
Rune Christiansen, Jonas Peters, 2020.
[abs][pdf][bib]      [code]

Optimal Bipartite Network Clustering
Zhixin Zhou, Arash A. Amini, 2020.
[abs][pdf][bib]

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang, 2020.
[abs][pdf][bib]

Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification
Leo L. Duan, 2020.
[abs][pdf][bib]      [code]

Causal Discovery Toolbox: Uncovering causal relationships in Python
Diviyan Kalainathan, Olivier Goudet, Ritik Dutta, 2020.
[abs][pdf][bib]      [code]

Noise Accumulation in High Dimensional Classification and Total Signal Index
Miriam R. Elman, Jessica Minnier, Xiaohui Chang, Dongseok Choi, 2020.
[abs][pdf][bib]      [code]

Learning with Fenchel-Young losses
Mathieu Blondel, André F.T. Martins, Vlad Niculae, 2020.
[abs][pdf][bib]      [code]

Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent
Dominic Richards, Patrick Rebeschini, 2020.
[abs][pdf][bib]

On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent
Huan Li, Zhouchen Lin, 2020.
[abs][pdf][bib]

Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
Mihai Cucuringu, Hemant Tyagi, 2020.
[abs][pdf][bib]

Generalized Nonbacktracking Bounds on the Influence
Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee, 2020.
[abs][pdf][bib]

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

The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response
Xin Zhang, Qing Mai, Hui Zou, 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, 2020.
[abs][pdf][bib]

A New Class of Time Dependent Latent Factor Models with Applications
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien, 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, 2020.
[abs][pdf][bib]

Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models
Ivona Bezáková, Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda, 2020.
[abs][pdf][bib]

Distributed Feature Screening via Componentwise Debiasing
Xingxiang Li, Runze Li, Zhiming Xia, Chen Xu, 2020.
[abs][pdf][bib]

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, 2020.
[abs][pdf][bib]

A Unified Framework for Structured Graph Learning via Spectral Constraints
Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar, 2020.
[abs][pdf][bib]      [code]

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, 2020.
[abs][pdf][bib]

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections
Junhong Lin, Volkan Cevher, 2020.
[abs][pdf][bib]

High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix
Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong, 2020.
[abs][pdf][bib]

Connecting Spectral Clustering to Maximum Margins and Level Sets
David P. Hofmeyr, 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, 2020.
[abs][pdf][bib]

Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta, 2020.
[abs][pdf][bib]

Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
Ery Arias-Castro, Adel Javanmard, Bruno Pelletier, 2020.
[abs][pdf][bib]

On lp-Support Vector Machines and Multidimensional Kernels
Victor Blanco, Justo Puerto, Antonio M. Rodriguez-Chia, 2020.
[abs][pdf][bib]

Generalized probabilistic principal component analysis of correlated data
Mengyang Gu, Weining Shen, 2020.
[abs][pdf][bib]      [code]

Neyman-Pearson classification: parametrics and sample size requirement
Xin Tong, Lucy Xia, Jiacheng Wang, Yang Feng, 2020.
[abs][pdf][bib]      [code]

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin, 2020.
[abs][pdf][bib]

Online Sufficient Dimension Reduction Through Sliced Inverse Regression
Zhanrui Cai, Runze Li, Liping Zhu, 2020.
[abs][pdf][bib]

On Mahalanobis Distance in Functional Settings
José R. Berrendero, Beatriz Bueno-Larraz, Antonio Cuevas, 2020.
[abs][pdf][bib]

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

Target Propagation in Recurrent Neural Networks
Nikolay Manchev, Michael Spratling, 2020.
[abs][pdf][bib]      [code]

Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms
Anna Little, Mauro Maggioni, James M. Murphy, 2020.
[abs][pdf][bib]      [code]

Lower Bounds for Parallel and Randomized Convex Optimization
Jelena Diakonikolas, Cristóbal Guzmán, 2020.
[abs][pdf][bib]

Universal Latent Space Model Fitting for Large Networks with Edge Covariates
Zhuang Ma, Zongming Ma, Hongsong Yuan, 2020.
[abs][pdf][bib]

A Model of Fake Data in Data-driven Analysis
Xiaofan Li, Andrew B. Whinston, 2020.
[abs][pdf][bib]

A Statistical Learning Approach to Modal Regression
Yunlong Feng, Jun Fan, Johan A.K. Suykens, 2020.
[abs][pdf][bib]

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, 2020.
[abs][pdf][bib]

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