JMLR Editorial Board
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.
- Andreas Christmann, Bayreuth University, Germany. Statistical machine learning theory, Kernel-based methods, Robust statistics.
- 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.
- Jennifer Dy, Northeastern University, USA. Clustering, feature selection, dimensionality reduction, graphical models, Bayesian methods, Bayesian Nonparametrics
- 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
- 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
- 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
- Alexander Rakhlin, University of Pennsylvania, USA. Learning theory, online learning, statistical learning theory
- 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.
- 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
- 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