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.
- 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, causality, 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.
- 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
- 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
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