JMLR Editorial Board

Editors-in-Chief

• Francis Bach, INRIA.
• David Blei, Columbia University.
• Bernhard Schölkopf, MPI for Intelligent Systems.

Managing Editor

• Aron Culotta, Illinois Institute of Technology, USA.

• 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
• 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
• David Barber, University College London, UK.
  Graphical models, deep learning, approximate inference
• 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
• Léon Bottou, Facebook AI Research.
  
• François Caron, University of Oxford, United Kingdom.
  Bayesian methods, Bayesian nonparametrics, Statistical network analysis, Monte Carlo methods, Probabilistic modeling
• Miguel A. Carreira-Perpinan, University of California, Merced, USA.
  optimization (nonconvex in particular), 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.
  
• Sanjoy Dasgupta, University of California, San Diego, USA.
  Unsupervised learning, semisupervised learning, active learning
• Inderjit S. Dhillon, University of Texas, Austin, USA.
  
• Jennifer Dy, Northeastern University, USA.
  clustering, feature selection, dimensionality reduction, graphical models, Bayesian methods, Bayesian Nonparametrics
• 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
• Moritz Hardt, Google Research, USA.
  Learning theory, algorithms, optimization, privacy
• Thomas Hofmann, ETH Zurich, Switzerland.
  Natural Language Learning, Text Mining, User Data Modeling, Large-scale learning
• 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
• Alex Ihler, University of California, Irvine, USA.
  graphical models, approximate inference, structured prediction
• Tommi Jaakkola, Massachusetts Institute of Technology, USA.
  Approximate inference, structured prediction, deep learning
• 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
• 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.
  
• 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
• 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, Microsoft Research, Cambridge, UK.
  structured prediction, computer vision, deep learning
• Una-May O'Reilly, Massachusetts Institute of Technology, USA.
  evolutionary algorithms, genetic programming,
• Manfred Opper, Technical University of Berlin, Germany.
  Bayesian methods, approximate inference, statistical mechanics, Gaussian processes
• Laurent Orseau, Google Deepmind, USA.
  Reinforcement Learning, Artificial General Intelligence
• Luis Ortiz, University of Michigan - Dearborn, USA.
  machine learning; computational game theory and economics; graphical models; artificial intelligence; applications to complex systems
• 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
• Alexander Rakhlin, University of Pennsylvania, USA.
  learning theory, online learning, statistical learning theory
• Ben Recht, University of California, Berkeley, USA.
  
• Lorenzo Rosasco, Massachusetts Institute of Technology, USA.
  Statistical learning theory, Optimization, Regularization, Inverse problems
• Saharon Rosset, Tel Aviv University, Israel.
  Bioinformatics and statistical genetics, statistical learning/predictive modeling, data mining/big data
• Ruslan Salakhutdinov, University of Toronto, Canada.
  
• Sujay Sanghavi, University of Texas, Austin, USA.
  
• 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
• 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
• Nathan Srebro, Toyota Technical Institute at Chicago, USA.
  
• 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
• Csaba Szepesvari, University of Alberta, Canada.
  Reinforcement learning, learning theory, online learning, learning interactively
• Olivier Teytaud, INRIA Saclay, France.
  
• 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.
  
• Zhuowen Tu, University of California at San Diego, USA.
  computer vision, deep learning, weakly-supervised learning
• 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
• Stefan Wrobel, Fraunhofer IAIS and University of Bonn, Germany.
  relational learning, graph mining, inductive logic programming, privacy-preserving learning, visual analytics, learning from spatial data, applications and big data
• Eric Xing, Carnegie Mellon University, USA.
  
• Tong Zhang, Baidu Inc, China.
  learning theory, optimization, large scale learning
• Zhihua Zhang, Peking University, China.
  Bayesian Analysis and Computations, Numerical Algebra and Optimization
• Hui Zou, University of Minnesota, USA.