JMLR Editorial Board
EditorsinChief
Managing Editors
Editorial Assistant
Production Editor
Web Master
JMLR Action Editors
 Ryan Adams, Princeton University, USA. Approximate Bayesian inference, graphical models, Markov chain Monte Carlo, variational inference, Bayesian nonparametrics, Bayesian optimization.
 Alekh Agarwal, Microsoft Research, USA. Reinforcement Learning, Online Learning, Bandits, Learning Theory.
 Shivani Agarwal, University of Pennsylvania, USA. Ranking and preference learning, choice modeling, supervised learning, multiclass learning, performance measures, loss functions.
 Edoardo M. Airoldi, Harvard University, USA. Statistics, approximate inference, causal inference, network data analysis, computational biology.
 Genevera Allen, Rice University, USA. Statistical machine learning, highdimensional statistics, modern multivariate analysis, graphical models, data integration, tensor decompositions, optimization.
 Pierre Alquier, Riken AIP, Japan. Statistical Learning theory, PACBayes learning, Approximate Bayesian inference, Variational inference; Highdimensional statistics.
 Anima Anandkumar, California Institute of Technology, USA. Tensor decomposition, nonconvex optimization, probabilistic models, reinforcement learning.
 Peter Auer, University of Leoben, Austria. Bandit problems, reinforcement learning, online learning.
 Samy Bengio, Google Research, USA. Deep learning, multiclass, ranking, sequences, speech and vision.
 Yoshua Bengio, Université de Montréal, Canada. Deep learning.
 Sébastien Bubeck, Microsoft Research, USA. Multiarmed bandits, online learning, adversarial examples.
 Joan Bruna, NYU, USA. Theory of deep learning, harmonic analysis, signal processing.
 Karsten Borgwardt, ETH Zurich, Switzerland. Feature Selection, pattern mining, graph mining, kernel methods, bioinformatics.
 Miguel A. CarreiraPerpinan, University of California, Merced, USA. Optimization (in particular for deep learning and decision trees), dimensionality reduction, meanshift algorithms, unsupervised learning.
 Victor Chernozhukov, MIT, USA. Causal Inference, Structural Equation Models, Econometrics, HighDimensional Central Limit Theorems, Optimal Transport
 Alexander Clark, King's College London, UK. Grammatical inference, unsupervised learning in NLP, natural language learning, mathematical linguistics.
 Corinna Cortes, Google Research, USA. Kernel methods, boosting, feature selection.
 Koby Crammer, Technion, Israel.
 John P. Cunningham, Columbia University, USA. State space models, deep generative models, approximate inference, gaussian processes, computational neuroscience.
 Marco Cuturi, Google. Optimal transport, geometric methods.
 Arnak Dalalyan, ENSAE / CREST, France. Robust estimation and learning, nonparametric inference, Langevin dynamics, PACBayes learning, Sparse learning and estimation.
 Tina EliassiRad, Northeastern University. Learning on complex networks, graph mining, network science
 Gal Elidan, Hebrew University, Israel.
 Charles Elkan, University of California at San Diego, USA.
 Barbara Engelhardt, Princeton University, USA. Latent factor models, computational biology, statistical inference, hierarchical models
 Rob Fergus, New York University, USA.
 Kenji Fukumizu, The Institute of Statistical Mathematics, Japan. Kernel methods, dimension reduction
 Russ Greiner, University of Alberta, Canada. Medical informatics, active/budgeted Learning
 Maya Gupta, Google Research, USA. Interpretable machine learning, clustering, regression, multitask learning, constrained optimization, stochastic gradient descent, largescale learning
 Isabelle Guyon, ClopiNet, USA. Feature selection, causaltity, model selection, automatic machine learning, computer vision, kernel method
 Stefan Harmeling, Heinrich Heine University Düsseldorf, Germany. deep learning, image processing, inverse problems, probabilistic machine learning, unsupervised learning.
 Zaid Harchaoui, University of Washington. Convex optimization, highdimensional statistics, kernelbased methods, nonconvex optimization, representation learning
 Philipp Hennig, University of Tübingen. Probabilistic Numerics (including Bayesian Optimization and Quadrature); Algorithms for Bayesian Inference; Algorithms for Deep Learning (Optimization, Sampling, etc.); Gaussian Processes; Sustainable ML and ML for Sustainability.
 Bert Huang, Virginia Tech, USA. structured prediction, probabilistic graphical models, relational learning, networks
 Aapo Hyvärinen, University of Helsinki, Finland. Unsupervised learning, natural image statistics, neuroimaging data analysis
 Tommi Jaakkola, Massachusetts Institute of Technology, USA. Approximate inference, structured prediction, deep learning
 Prateek Jain, Microsoft Research, India. Nonconvex Optimization, Stochastic Optimization, Largescale Optimization, Resourceconstrained Machine Learning
 Stefanie Jegelka, Massachusetts Institute of Technology, USA. Submodularity, determinantal point processes, negative dependence, Bayesian optimization
 David Jensen, University of Massachusetts Amherst, USA. Causal modeling, causal inference, relational learning, explainability.
 Samuel Kaski, Aalto University, Finland. Probabilistic modelling, multiple data sources (multiview, multitask, multimodal, retrieval); applications in bioinformatics, user interaction, brain signal analysis
 Sathiya Keerthi, Microsoft Research, USA. optimization, large margin methods, structured prediction, large scale learning, distributed training
 Emtiyaz Khan, RIKEN Center for Advanced Intelligence, Japan. Variational Inference, Approximate Bayesian inference, Bayesian Deep Learning
 George Konidaris, Duke University, USA. Reinforcement Learning, artificial intelligence, robotics
 Mladen Kolar, University of Chicago Booth School of Business, USA. Highdimensional statistics, probabilistic graphical models, statistical machine learning, model selection.
 Samory K. Kpotufe, Columbia University, USA. Statistical Learning Theory, Nonparametric Estimation and Inference, Domain Adaptation, Clustering, Active Learning
 Andreas Krause, ETH Zurich, Switzerland. Active learning, Bayesian optimization, submodularity, sequential decision making
 Sanjiv Kumar, Google Research, USA. largescale learning, hashing, matrix factorization, nearest neighbor search, clustering
 Anthony Lee, University of Bristol, UK. Markov chain Monte Carlo, sequential Monte Carlo.
 Simon LacosteJulien, Université de Montréal and Mila, Canada. optimization, structured prediction, theory of deep learning
 Christoph Lampert, Institute of Science and Technology, Austria. transfer learning, structured prediction, computer vision
 Honglak Lee, Google and University of Michigan, Ann Arbor. Deep Learning, Deep Generative Models, Representation Learning, Reinforcement Learning, Unsupervised Learning
 Daniel Lee, University of Pennsylvania, USA. Unsupervised learning, reinforcement learning, robotics, computational neuroscience
 Qiang Liu, Dartmouth College, USA. Probablistic graphical models, inference and learning, computational models for crowdsourcing
 Gábor Lugosi, Pompeu Fabra University, Spain. learning theory, online prediction
 Michael Mahoney, University of California at Berkeley, USA.
 Vikash K. Mansinghka, Massachusetts Institute of Technology, USA. Probabilistic programming, artificial intelligence, probabilistic machine learning, Bayesian methods
 Julien Mairal, INRIA. Convex optimization, sparse estimation, statistical signal and image processing, representation learning.
 Shie Mannor, Technion, Israel. Reinforcement learning, bandit problems, learning theory, learning in games
 Benjamin Marlin, University of Massachusetts Amherst. Time series, bayesian deep learning, health applications.
 Jon McAuliffe, University of California at Berkeley, USA. approximate inference, supervised learning, causal inference, sequential analysis, reinforcement learning
 Robert E. McCulloch, University of Chicago, USA.
 Qiaozhu Mei, University of Michigan, USA. Learning from text, network, and behavioral data, representation learning, interactive learning
 Vahab Mirrokni, Google Research, USA. Mechanism Desgin and Internet Economics, Algorithmic Game Thoery, Distributed Optimization, Submodular Optimization, Largescale Graph Mining
 Shakir Mohamed, Deepmind. Generative models, variational inference, bayesian machine learning, unsupervised learning, probabilistic modelling, computers and society, ethics, healthcare, environmental science.
 Mehryar Mohri, New York University, USA. Learning theory (all aspects, including auctioning, ensemble methods, structured prediction, time series, online learning, games, adaptation, learning kernels, spectral learning, ranking, lowrank approximation)
 Joris Mooij, University of Amsterdam, Netherlands. Causality
 Sayan Mukherjee, Duke University, USA. Bayesian methodology, computational biology, inference in dynamical systems, geometry and topology in inference, stochastic geometry and topology
 Boaz Nadler, Weizmann Institute of Science, Israel. High dimensional statistics, sparsity, latent variable models, ensemble learning
 Sebastian Nowozin, Google Research. Structured prediction, computer vision, deep learning
 UnaMay O'Reilly, Massachusetts Institute of Technology, USA. evolutionary algorithms, genetic programming,
 Laurent Orseau, Google Deepmind, USA. Reinforcement Learning, Artificial General Intelligence
 Jie Peng, University of California, Davis, USA. High dimensional statistical inference, graphical models, functional data analysis
 Jan Peters, Technische Universitaet Darmstadt, Germany. Reinforcement learning, robot learning, policy search
 Avi Pfeffer, Charles River Analytics, USA. Probabilistic programming, probabilistic reasoning, cyber security
 Joelle Pineau, McGill University, Canada. Reinforcement learning, deep learning, robotics
 Massimiliano Pontil, Istituto Italiano di Tecnologia (Italy), University College London (UK). Multitask and transfer learning, convex optimization, kernel methods, sparsity regularization
 Luc de Raedt, Katholieke Universiteit Leuven, Belgium. (statistical) relational learning, inductive logic programming, symbolic machine learning, probabilistic programming, learning from structured data, pattern mining
 Pradeep Ravikumar, Carnegie Mellon University, USA. Statistical Learning Theory, Graphical Models, Robustness, Explainability
 Garvesh Raskutti, University of WisconsinMadison, USA. Highdimensional statistics, time series, graphical models, optimization
 Lorenzo Rosasco, Massachusetts Institute of Technology, USA. Statistical learning theory, Optimization, Regularization, Inverse problems
 Ruslan Salakhutdinov, University of Toronto, Canada.
 Sivan Sabato, Ben Gurion University of the Negev, Israel. Statistical learning theory, active learning, interactive learning.
 Marc Schoenauer, INRIA Saclay, France. Stochastic Optimization, Derivativefree Optimization, Evolutionary Algorithms, Algorithm configuration/selection
 Ohad Shamir, Weizmann Institute of Science, Israel. Learning theory, optimization, theory of deep learning.
 John ShaweTaylor, University College London, UK. Statistical learning theory, kernel methods, reinforcement learning.
 Christian R. Shelton, UC Riverside, USA. Time series, temporal and spatial processes, point processes
 Xiaotong Shen, University of Minnesota, USA. Learning, Graphical models, Recommenders
 David Sontag, Massachusetts Institute of Technology. Graphical models, approximate inference, structured prediction, unsupervised learning, applications to health care
 Peter Spirtes, Carnegie Mellon University, USA. Bayesian networks, Causal models, Model search, Causal inference
 Suvrit Sra, Massachusetts Institute of Technology, USA. Optimization, Deep Learning Theory, Nonconvex Optimization, nonEuclidean geometry and optimization, Negative Dependence, Optimal Transport, Matrix Theory, Determinantal point processes
 Ingo Steinwart, University of Stuttgart, Germany. Statistical learning theory, Kernelbased learning methods (support vector machines), Cluster Analysis, Loss functions
 Amos Storkey, University of Edinburgh, UK. Deep Generative Models, Adversarial Learning, Probabilistic Models, Learning under Constraints, Transfer Learning, Prediction Markets
 Erik Sudderth, University of California, Irvine, USA. Bayesian nonparametrics, graphical models, unsupervised learning, variational inference, Monte Carlo methods, computer vision, signal and image processing.
 Csaba Szepesvari, University of Alberta, Canada. Reinforcement learning, learning theory, online learning, learning interactively
 Ambuj Tewari, University of Michigan. Statistical learning theory, online learning, bandit problems, reinforcement learning, highdimensional statistics, optimization.
 Ivan Titov, University of Amsterdam, Netherlands. Natural language processing, representation learning, structured prediction, graphical models
 Koji Tsuda, National Institute of Advanced Industrial Science and Technology, Japan.
 Nicolas Vayatis, ENS Cachan, France. Statistical learning theory
 JeanPhilippe Vert, Google Research, France. kernel methods, computational biology, statistical learning theory
 Silvia Villa, Genova University, Italy. Convex optimization, first order methods, regularizatio
 Manfred Warmuth, University of California at Santa Cruz, USA.
 Kilian Weinberger, Cornell University, USA. Deep Learning, Representation Learning, Ranking, Computer Vision
 David Wipf, Amazon. Deep generative models, Bayesian inference, sparse estimation, representation learning.
 Daniela Witten, University of Washington. highdimensional, statistics, sparsity, genomics, neuroscience
 Frank Wood, University of British Columbia. Probabilistic programming, artificial intelligence, probabilistic machine learning, Bayesian methods
 Tong Zhang, Baidu Inc, China. learning theory, optimization, large scale learning
 Zhihua Zhang, Peking University, China. Bayesian Analysis and Computations, Numerical Algebra and Optimization
 Ji Zhu, University of Michigan, USA. Network data analysis, latent variable models, graphical models, highdimensional data, health analytics.
JMLRMLOSS Editors
 Alexandre Gramfort, INRIA, Université ParisSaclay, France. supervised learning, convex optimization, sparse methods, machine learning software, applications in neuroscience
 Antti Honkela, University of Helsinki, Finland. Probabilistic modelling, approximate inference, bioinformatics
 Andreas C. Müller, Microsoft, USA. Supervised learning, machine learning software, automatic machine learning
 Joaquin Vanschoren, Eindhoven University of Technology, Netherlands. Automated machine learning, metalearning, machine learning software.
JMLR Advisory Board

ShunIchi Amari, RIKEN Brain Science Institute, Japan

Andrew Barto, University of Massachusetts at Amherst, USA

Thomas Dietterich, Oregon State University, USA

Jerome Friedman, Stanford University, USA

Stuart Geman, Brown University, USA

Geoffrey Hinton, University of Toronto, Canada

Michael Jordan, University of California at Berkeley at USA

Leslie Pack Kaelbling, Massachusetts Institute of Technology, USA

Michael Kearns, University of Pennsylvania, USA

Steven Minton, InferLink, USA

Tom Mitchell, Carnegie Mellon University, USA

Stephen Muggleton, Imperial College London, UK

Kevin Murphy, Google, USA

Nils Nilsson, Stanford University, USA

Tomaso Poggio, Massachusetts Institute of Technology, USA

Ross Quinlan, Rulequest Research Pty Ltd, Australia

Stuart Russell, University of California at Berkeley, USA

Lawrence Saul, University of California at San Diego, USA

Terrence Sejnowski, Salk Institute for Biological Studies, USA

Richard Sutton, University of Alberta, Canada

Leslie Valiant, Harvard University, USA