JMLR Editorial Board

Editors-in-Chief

Managing Editors

Editorial Assistant

Production Editor

Production Staff

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.
  • 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, high-dimensional statistics, modern multivariate analysis, graphical models, data integration, tensor decompositions, optimization.
  • Anima Anandkumar, California Institute of Technology, USA. Tensor decomposition, non-convex optimization, probabilistic models, reinforcement learning.
  • Peter Auer, University of Leoben, Austria. Bandit problems, reinforcement learning, online learning.
  • Samy Bengio, Google Research, USA. Deep learning, multi-class, ranking, sequences, speech and vision.
  • Yoshua Bengio, Université de Montréal, Canada. Deep learning.
  • Jeff Bilmes, University of Washington, USA.
  • Karsten Borgwardt, ETH Zurich, Switzerland. Feature Selection, pattern mining, graph mining, kernel methods, bioinformatics.
  • Miguel A. Carreira-Perpinan, University of California, Merced, USA. Optimization (in particular for deep learning and decision trees), dimensionality reduction, mean-shift algorithms, unsupervised learning.
  • 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.
  • Arnak Dalalyan, ENSAE / CREST, France. Robust estimation and learning, nonparametric inference, Langevin dynamics, PAC-Bayes learning, Sparse learning and estimation.
  • Sanjoy Dasgupta, University of California, San Diego, USA. Unsupervised learning, semisupervised learning, active learning.
  • Inderjit S. Dhillon, University of Texas, Austin, USA.
  • Tina Eliassi-Rad, 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
  • Amir Globerson, Tel Aviv University, Israel. Graphical models, approximate inference, structured prediction, natural language processing
  • Moises Goldszmidt, Microsoft Research, USA.
  • Russ Greiner, University of Alberta, Canada. Medical informatics, active/budgeted Learning
  • Arthur Gretton, University College London, UK. Hypothesis testing, kernel methods
  • Maya Gupta, Google Research, USA. Interpretable machine learning, clustering, regression, multi-task learning, constrained optimization, stochastic gradient descent, large-scale learning
  • Isabelle Guyon, ClopiNet, USA. Feature selection, causaltity, model selection, automatic machine learning, computer vision, kernel method
  • Zaid Harchaoui, University of Washington. Convex optimization, high-dimensional statistics, kernel-based methods, non-convex optimization, representation learning
  • Moritz Hardt, Google Research, USA. Learning theory, algorithms, optimization, privacy
  • 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
  • Stefanie Jegelka, Massachusetts Institute of Technology, USA. Submodularity, determinantal point processes, negative dependence, Bayesian optimization
  • Samuel Kaski, Aalto University, Finland. Probabilistic modelling, multiple data sources (multi-view, multi-task, 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
  • Andreas Krause, ETH Zurich, Switzerland. Active learning, Bayesian optimization, submodularity, sequential decision making
  • Sanjiv Kumar, Google Research, USA. large-scale learning, hashing, matrix factorization, nearest neighbor search, clustering
  • 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
  • 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.
  • Chris Meek, Microsoft Research, USA. Graphical Models, recommendation systems, point processes
  • 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, Large-scale Graph Mining
  • Mehryar Mohri, New York University, USA. Learning theory (all aspects, including auctioning, ensemble methods, structured prediction, time series, on-line learning, games, adaptation, learning kernels, spectral learning, ranking, low-rank 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
  • Long Nguyen, University of Michigan, USA. Bayesian nonparametrics, hierarchical and graphical models, variational and geometric methods for statistical inference
  • Sebastian Nowozin, Google Research. Structured prediction, computer vision, deep learning
  • Una-May 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
  • Pushmeet Kohli, DeepMind. Adversarial evaluation and robustness, program synthesis, graph neural networks
  • Luc de Raedt, Katholieke Universiteit Leuven, Belgium. (statistical) relational learning, inductive logic programming, symbolic machine learning, probabilistic programming, learning from structured data, pattern mining
  • Garvesh Raskutti, University of Wisconsin-Madison, USA. High-dimensional statistics, time series, graphical models, optimization
  • Ben Recht, University of California, Berkeley, USA.
  • Lorenzo Rosasco, Massachusetts Institute of Technology, USA. Statistical learning theory, Optimization, Regularization, Inverse problems
  • Ruslan Salakhutdinov, University of Toronto, Canada.
  • Sujay Sanghavi, University of Texas, Austin, USA. Sparsity, Convex and Non-Convex Optimization, Statistical Learning, Graphical Models, Matrix Factorization
  • Mark Schmidt, University of British Columbia, Canada. Convex Optimization, Probabilistic Graphical Models
  • Marc Schoenauer, INRIA Saclay, France. Stochastic Optimization, Derivative-free Optimization, Evolutionary Algorithms, Algorithm configuration/selection
  • Ohad Shamir, Weizmann Institute of Science, Israel. Learning theory, optimization, theory of deep learning.
  • John Shawe-Taylor, University College London, UK. Statistical learning theory, kernel methods, reinforcement learning.
  • 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, Non-convex Optimization, non-Euclidean geometry and optimization, Negative Dependence, Optimal Transport, Matrix Theory, Determinantal point processes
  • Ingo Steinwart, University of Stuttgart, Germany. Statistical learning theory, Kernel-based 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
  • Olivier Teytaud, INRIA Saclay, France.
  • Ambuj Tewari, University of Michigan. Statistical learning theory, online learning, bandit problems, reinforcement learning, high-dimensional statistics, optimization.
  • Ivan Titov, University of Amsterdam, Netherlands. Natural language processing, representation learning, structured prediction, graphical models
  • Ryan Tibshirani, Carnegie Mellon University. High-dimensional statistics, nonparametric estimation, selective inference, convex optimization
  • Ryota Tomioka, Microsoft Research Cambridge, UK. Optmization, tensor decomposition, neural networks
  • Koji Tsuda, National Institute of Advanced Industrial Science and Technology, Japan.
  • Nicolas Vayatis, ENS Cachan, France. Statistical learning theory
  • S V N Vishwanathan, Purdue University, USA. Kernels, Optimization, Distributed computing, Applications
  • Manfred Warmuth, University of California at Santa Cruz, USA.
  • Kilian Weinberger, Cornell University, USA. Deep Learning, Representation Learning, Ranking, Computer Vision
  • David Wipf, Microsoft Research Asia, China. Bayesian learning, sparse estimation, computer vision
  • Daniela Witten, University of Washington. high-dimensional, 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

JMLR-MLOSS Editors

  • Alexandre Gramfort, INRIA, Université Paris-Saclay, France. supervised learning, convex optimization, sparse methods, machine learning software, applications in neuroscience
  • Antti Honkela, University of Helsinki, Finland. Probabilistic modelling, approximate inference, bioinformatics
  • Balázs Kégl, CNRS / Université Paris-Saclay, France. Ensemble methods, hyperparameter optimization, applications in particle and astrophysics
  • Andreas C. Müller, Columbia University, USA. Supervised learning, machine learning software, automatic machine learning

JMLR Editorial Board

  • Naoki Abe, IBM TJ Watson Research Center, USA
  • Yasemin Altun, Google Inc, Switzerland
  • Jean-Yves Audibert, CERTIS, France
  • Jonathan Baxter, Australia National University, Australia
  • Richard K. Belew, University of California at San Diego, USA
  • Kristin Bennett, Rensselaer Polytechnic Institute, USA
  • Christopher M. Bishop, Microsoft Research, Cambridge, UK
  • Lashon Booker, The Mitre Corporation, USA
  • Henrik Boström, Stockholm University/KTH, Sweden
  • Craig Boutilier, Google Research, USA
  • John Patrick Cunningham, Columbia University, USA
  • Nello Cristianini, University of Bristol, UK
  • Peter Dayan, University College, London, UK
  • Dennis DeCoste, eBay Research, USA
  • Thomas Dietterich, Oregon State University, USA
  • Saso Dzeroski, Jozef Stefan Institute, Slovenia
  • Ran El-Yaniv, Technion, Israel
  • Peter Flach, Bristol University, UK
  • Dan Geiger, Technion, Israel
  • Claudio Gentile, Università degli Studi dell'Insubria, Italy
  • Sally Goldman, Google Research, USA
  • Thore Graepel, Google DeepMind and University College London, UK
  • Tom Griffiths, University of California at Berkeley, USA
  • Carlos Guestrin, University of Washington, USA
  • Stefan Harmeling, University of Düsseldorf, Germany
  • David Heckerman, Microsoft Research, USA
  • Katherine Heller, Duke University, USA
  • Philipp Hennig, MPI for Intelligent Systems, Germany
  • Larry Hunter, University of Colorado, USA
  • Jens Kober, Delft University of Technology, Netherlands
  • Risi Kondor, University of Chicago, USA
  • Aryeh Kontorovich, Ben-Gurion University of the Negev, Israel
  • Samory Kpotufe, Princeton University, USA
  • John Lafferty, University of Chicago, USA
  • Erik Learned-Miller, University of Massachusetts, Amherst, USA
  • Fei Fei Li, Stanford University, USA
  • Yi Lin, University of Wisconsin, USA
  • Wei-Yin Loh, University of Wisconsin, USA
  • Richard Maclin, University of Minnesota, USA
  • Sridhar Mahadevan, University of Massachusetts, Amherst, USA
  • Vikash Mansingkha, Massachusetts Institute of Technology, USA
  • Yishay Mansour, Tel-Aviv University, Israel
  • Jon McAuliffe, University of California, Berkeley, USA
  • Andrew McCallum, University of Massachusetts, Amherst, USA
  • Raymond J. Mooney, University of Texas, Austin, USA
  • Klaus-Robert Muller, Technical University of Berlin, Germany
  • Kevin Murphy, Google, USA
  • Guillaume Obozinski, Ecole des Ponts - ParisTech, France
  • Pascal Poupart, University of Waterloo, Canada
  • Konrad Rieck, University of Göttingen, Germany
  • Cynthia Rudin, Massachusetts Institute of Technology, USA
  • Suchi Saria, Johns Hopkins University, USA
  • Robert Schapire, Princeton University, USA
  • Fei Sha, University of Southern California, USA
  • Shai Shalev-Shwartz, Hebrew University of Jerusalem, Israel
  • Padhraic Smyth, University of California, Irvine, USA
  • Bharath Sriperumbudur, Pennsylvania State University, USA
  • Alexander Statnikov, New York University, USA
  • Jean-Philippe Vert, Mines ParisTech, France
  • Martin J. Wainwright, University of California at Berkeley, USA
  • Chris Watkins, Royal Holloway, University of London, UK
  • Max Welling, University of Amsterdam, Netherlands
  • Stefan Wrobel, Fraunhofer IAIS and University of Bonn, Germany
  • Chris Williams, University of Edinburgh, UK
  • Alice Zheng, GraphLab, USA

JMLR Advisory Board

  • Shun-Ichi 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