JMLR Editorial Board
Editors-in-ChiefFrancis Bach, INRIA
David Blei, Columbia University
Bernhard Schölkopf, MPI for Intelligent Systems
Aron Culotta, Illinois Institute of Technology, USA
Alp Kucukelbir, Fero Labs and Columbia University
Trevor Campbell, Massachusetts Institute of Technology
Behzad Tabibian, MPI for Intelligent Systems
Hoon Cho, Massachusetts Institute of Technology
Fabian Pedregosa, UC Berkeley
JMLR Action Editors
- Ryan Adams, Princeton University, USA. Approximate Bayesian inference, graphical models, Markov chain Monte Carlo, variational inference, Bayesian nonparametrics, Bayesian optimization.
- Edoardo M. Airoldi, Harvard University, USA. Statistics, Approximate inference, Causal inference, Network data analysis, Computational biology
- Anima Anandkumar, University of California Irvine, USA. tensor decomposition, non-convex optimization, probabilistic models, reinforcement learning
- Peter Auer, Montanuniversitaet Leoben, Austria. Bandit problems, reinforcement learning, online learning
- David Barber, University College London, UK. Graphical models, deep learning, approximate inference
- Mikhail Belkin, Ohio State University, USA.
- 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.
- Emily Fox, University of Washington, USA.
- Kenji Fukumizu, The Institute of Statistical Mathematics, Japan. kernel methods, dimension reduction
- Sara van de Geer, ETH Zürich, Switzerland. theory of high-dimensional statistics, regularization
- 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
- Matthias Hein, Saarland University, Germany. Unsupervised Learning (constrained clustering, manifold learning), multi-task/multi-class learning, learning on graphs/networks
- 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
- Gert Lanckriet, University of California, San Diego, USA. recommendation algorithms and systems, large-scale learning, learning from multimodal data, music and video analysis and understanding, information retrieval, context prediction from wearable sensors.
- 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
- Ulrike von Luxburg, University of Tübingen, Germany. theory of unsupervised learning, networks
- 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 and algorithms [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.
- 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
- Martin Pelikan, Google Inc, USA.
- 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
- Le Song, Georgia Institute of Technology, USA. kernel methods, network analysis, stochastic process
- David Sontag, New York University, USA. 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
- 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
- Nikos Vlassis, Netflix, USA. POMDPs, reinforcement learning, recommendation algorithms
- 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
- 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.
- 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
- Cheng Soon Ong, Australian National University, Australia. Experimental design, feature learning, ranking, bioinformatics
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
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
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