JMLR Editorial Board
Editors-in-Chief
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, high-dimensional statistics, modern multivariate analysis, graphical models, data integration, tensor decompositions, optimization.
- Pierre Alquier, Riken AIP, Japan. Statistical Learning theory, PAC-Bayes learning, Approximate Bayesian inference, Variational inference; High-dimensional statistics.
- 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.
- Sébastien Bubeck, Microsoft Research, USA. Multi-armed 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. Carreira-Perpinan, University of California, Merced, USA. Optimization (in particular for deep learning and decision trees), dimensionality reduction, mean-shift algorithms, unsupervised learning.
- Victor Chernozhukov, MIT, USA. Causal Inference, Structural Equation Models, Econometrics, High-Dimensional 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, PAC-Bayes learning, Sparse learning and estimation.
- 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
- Russ Greiner, University of Alberta, Canada. Medical informatics, active/budgeted Learning
- 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
- 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, high-dimensional statistics, kernel-based methods, non-convex 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. Non-convex Optimization, Stochastic Optimization, Large-scale Optimization, Resource-constrained 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 (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
- Mladen Kolar, University of Chicago Booth School of Business, USA. High-dimensional 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. large-scale learning, hashing, matrix factorization, nearest neighbor search, clustering
- Anthony Lee, University of Bristol, UK. Markov chain Monte Carlo, sequential Monte Carlo.
- Simon Lacoste-Julien, 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, Large-scale 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, 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
- 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
- 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 Wisconsin-Madison, USA. High-dimensional 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, 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.
- 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, 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
- 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
- Koji Tsuda, National Institute of Advanced Industrial Science and Technology, Japan.
- Nicolas Vayatis, ENS Cachan, France. Statistical learning theory
- Jean-Philippe 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. 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
- Ji Zhu, University of Michigan, USA. Network data analysis, latent variable models, graphical models, high-dimensional data, health analytics.
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
- Andreas C. Müller, Microsoft, USA. Supervised learning, machine learning software, automatic machine learning
- Joaquin Vanschoren, Eindhoven University of Technology, Netherlands. Automated machine learning, meta-learning, machine learning software.
JMLR Advisory Board
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Shun-Ichi Amari, RIKEN Brain Science Institute, Japan
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Andrew Barto, University of Massachusetts at Amherst, USA
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Thomas Dietterich, Oregon State University, USA
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Jerome Friedman, Stanford University, USA
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Stuart Geman, Brown University, USA
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Geoffrey Hinton, University of Toronto, Canada
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Michael Jordan, University of California at Berkeley at USA
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Leslie Pack Kaelbling, Massachusetts Institute of Technology, USA
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Michael Kearns, University of Pennsylvania, USA
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Steven Minton, InferLink, USA
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Tom Mitchell, Carnegie Mellon University, USA
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Stephen Muggleton, Imperial College London, UK
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Kevin Murphy, Google, USA
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Nils Nilsson, Stanford University, USA
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Tomaso Poggio, Massachusetts Institute of Technology, USA
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Ross Quinlan, Rulequest Research Pty Ltd, Australia
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Stuart Russell, University of California at Berkeley, USA
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Lawrence Saul, University of California at San Diego, USA
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Terrence Sejnowski, Salk Institute for Biological Studies, USA
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Richard Sutton, University of Alberta, Canada
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Leslie Valiant, Harvard University, USA
