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, Google Research, USA. Reinforcement Learning, Online Learning, Bandits, Learning Theory.
  • Shipra Agrawal, Columbia University Reinforcement Learning, Multi-armed bandits, Online learning, Online optimization
  • Dan Alistarh, IST Austria & Neural Magic distributed optimization, federated learning, model compression, efficient ML
  • Genevera Allen, Rice University, USA Statistical machine learning, high-dimensional statistics, modern multivariate analysis, graphical models, data integration, tensor decompositions.
  • Pierre Alquier, ESSEC Asia-Pacific Statistical Learning theory, PAC-Bayes learning, Approximate Bayesian inference, Variational inference, High-dimensional statistics
  • Animashree Anandkumar, California Institute of Technology, USA Tensor decomposition, non-convex optimization, probabilistic models, reinforcement learning.
  • Krishnakumar Balasubramanian, University of California, Davis Sampling and stochastic optimization, Kernel methods, Geometric and topological data analysis, Statistical learning theory.
  • Elias Bareinboim, Columbia University causal Inference, generalizability, fairness, reinforcement learning
  • Marc Bellemare, Google Research, Canada Reinforcement learning, deep learning, representation learning, exploration
  • Yoshua Bengio, University of Montreal, Canada / Mila Deep learning, learning to reason
  • Samy Bengio, Apple, USA Deep learning, representation learning
  • Quentin Berthet, Google Research High-dimensional statistics, convex optimization, differentiable programming
  • Alexandre Bouchard, UBC mcmc, smc, phylogenetics
  • Joan Bruna, NYU, USA deep learning theory, signal processing, statistics
  • Miguel Carreira-Perpinan, University of California, Merced, USA Decision trees and forests, neural network compression, optimization in deep learning
  • Silvia Chiappa, DeepMind Causal inference, Approximate Bayesian inference, variational inference, ML fairness
  • Alexander Clark, University of Gothenburg grammatical inference, natural language processing
  • Corinna Cortes, Google Research, USA Kernel methods, boosting, feature selection.
  • John Cunningham, Columbia University, USA State space models, deep generative models, approximate inference, gaussian processes, computational neuroscience.
  • Marco Cuturi, Apple Optimal transport, geometric methods
  • Florence d'Alche-Buc, Telecom Paris, Institut Polytechnique de Paris Kernel methods, complex output prediction, robustness, explainability, bioinformatics
  • Luc De Raedt, Katholieke Universiteit Leuven, Belgium (statistical) relational learning, inductive logic programming, symbolic machine learning, probabilistic programming, learning from structured data, pattern mining
  • Marc Peter Deisenroth, University College London Gaussian processes, Bayesian optimization, Meta learning, Bayesian inference
  • Vanessa Didelez, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany causal inference, graphical models, structure learning, applications in epidemiology
  • justin domke, University of Massachusetts Amherst probabilistic methods, variational inference, bayesian inference
  • Gal Elidan, Hebrew University, Israel
  • Barbara Engelhardt, Stanford University, USA Latent factor models, computational biology, statistical inference, hierarchical models
  • Kenji Fukumizu, The Institute of Statistical Mathematics, Japan Kernel methods, dimension reduction
  • Aurelien Garivier, Ecole Normale Suprieure de Lyon, France Bandits, Sequential Analysis, Information Theory and Statistics
  • Christophe Giraud, Universite Paris-Saclay Clustering, network analysis, algorithmic fairness, active learning, theory of neural networks, high-dimensional statistics
  • Manuel Gomez-Rodriguez, Max Planck Institute for Software Systems Fairness, interpretability, accountability, strategic behavior, human-ai collaboration, temporal point processes
  • Russ Greiner, University of Alberta, Canada Medical informatics, active/budgeted Learning
  • Benjamin Guedj, Inria and University College London, France and UK Learning theory, PAC-Bayes, computational statistics, high-dimensional statistics, theory of deep learning, probabilistic models, Bayesian inference
  • Rajarshi Guhaniyogi, Texas A & M University Spatial and spatio-temporal Bayesian methods for large data, Bayes theory and methods for high dimensional regressions, tensor and network-valued regressions, functional data analysis, approximate Bayesian inference, graphical models, applications in neuroimaging and environmental sciences
  • Maya Gupta, University of Washington fairness, interpretability, societal issues, safety, regresssion, ensembles, shape constraints, immunology, information theory
  • Isabelle Guyon, University Paris-Saclay, France, and ChaLearn, USA Feature selection, causality, model selection, automated machine learning, computer vision, kernel methods, privacy, fairness
  • Matthew Hoffman, Google Bayesian inference, Markov chain Monte Carlo, Sequential Monte Carlo,Variational inference
  • Daniel Hsu, Columbia University Learning theory
  • Aapo Hyvarinen, 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
  • Martin Jaggi, EPFL, Switzerland Distributed training, federated learning, optimization
  • 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
  • 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
  • Mohammad Emtiyaz Khan, RIKEN Center for Advanced Intelligence, Japan Variational Inference, Approximate Bayesian inference, Bayesian Deep Learning
  • Mladen Kolar, University of Chicago Booth School of Business, USA high-dimensional statistics, graphical models
  • George Konidaris, Duke University, USA Reinforcement Learning, artificial intelligence, robotics
  • Aryeh Kontorovich, Ben-Gurion University metric spaces, nearest neighbors, Markov chains, statistics
  • Sanmi Koyejo, Stanford University federated learning, distributed machine learning, robust machine learning, statistical learning theory, neuroimaging, machine learning for medical imaging, machine learning for healthcare
  • Samory Kpotufe, Columbia University, USA Statistical Learning Theory, Nonparametric Estimation and Inference, Domain Adaptation, Clustering, Active Learning
  • Sanjiv Kumar, Google Research representation learning, optimization, deep learning, hashing, nearest neighbor search
  • Eric Laber, Duke University reinforcement learning, precision medicine, treatment regimes, causal inference
  • Simon Lacoste-Julien, Mila, Universit de Montral & SAIT AI Lab, Montreal optimization, structured prediction, theory of deep learning
  • Christoph Lampert, Institute of Science and Technology, Austria (IST Austria) transfer learning, trustworthy learning, computer vision
  • Tor Lattimore, DeepMind Bandits, reinforcement learning, online learning
  • Alessandro Lazaric, Meta AI Reinforcement learning, Bandit
  • Honglak Lee, Google and University of Michigan, Ann Arbor Deep Learning, Deep Generative Models, Representation Learning, Reinforcement Learning, Unsupervised Learning
  • Anthony Lee, University of Bristol Markov chain Monte Carlo, sequential Monte Carlo
  • Qiang Liu, Dartmouth College, USA Probablistic graphical models, inference and learning, computational models for crowdsourcing
  • Po-Ling Loh, University of Cambridge high-dimensional statistics, robust statistics, differential privacy, graphical models, nonconvex optimization
  • Gabor Lugosi, Pompeu Fabra University, Spain statistical learning theory, online prediction, concentration inequalities
  • Michael Mahoney, University of California at Berkeley, USA randomized linear algebra; stochastic optimization; neural networks; matrix algorithms; graph algorithms; scientific machine learning
  • Vikash Mansinghka, Massachusetts Institute of Technology, USA Probabilistic programming, Bayesian structure learning, large-scale sequential Monte Carlo
  • Benjamin Marlin, University of Massachusetts Amherst Probabilistic models, missing data, time series
  • Qiaozhu Mei, University of Michigan, USA Learning from text, network, and behavioral data, representation learning, interactive learning
  • Vahab Mirrokni, Google Research Mechanism Desgin and Internet Economics, Algorithmic Game Theory, 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; University of Leipzig; Max Planck Institute for Mathematics in the Sciences Bayesian, Time series, Geometry, Topology, Deep learning
  • Boaz Nadler, Weizmann Institute of Science, Israel High dimensional statistics, sparsity, latent variable models, ensemble learning
  • Gergely Neu, reinforcement learning, learning theory, online learning, bandit theory
  • Lam Nguyen, IBM Research, Thomas J. Watson Research Center Stochastic Gradient Algorithms, Non-convex Optimization, Stochastic Optimization, Convex Optimization
  • Scott Niekum, University of Massachusetts Amherst AI safety, imitation learning, reinforcement learning, robotics, human-ai interaction
  • Chris Oates, Newcastle University Bayesian computation, kernel methods, uncertainty quantification
  • Francesco Orabona, Boston University Online convex optimization, betting algorithms, parameter-free online optimization, stochastic optimization
  • Laurent Orseau, Deepmind Reinforcement Learning, Artificial General Intelligence
  • Debdeep Pati, Texas A&M University Bayes theory and methods in high dimensions; Approximate Bayesian methods; high dimensional network analysis, graphical models, hierarchical modeling of complex shapes, point pattern data modeling, real-time tracking algorithms
  • Jie Peng, University of California, Davis, USA High dimensional statistical inference, graphical models, functional data analysis
  • Vianney Perchet, ENSAE & Criteo AI Lab bandits, online learning, matching
  • Alexandre Proutiere, KTH Royal Institute of Technology Reinforcement learning, statistical learning in control systems, bandits, clustering and community detection
  • Maxim Raginsky, University of Illinois at Urbana-Champaign Theory of deep learning, statistical learning, optimization, applied probability, concentration of measure, dynamical systems and control
  • Pradeep Ravikumar, Carnegie Mellon University, USA Statistical Learning Theory, Graphical Models, Robustness, Explainability
  • Lorenzo Rosasco, University of Genova, Italy and Massachusetts Institute of Technology, USA Statistical learning theory, Optimization, Regularization, Inverse problems
  • Daniel Roy, University of Toronto generalization, learning theory, deep learning, pac-bayes, nonparametric bayes, online learning, nonvacuous bounds
  • Ruslan Salakhutdinov, Carnegie Mellon University Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization.
  • 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 Shelton, UC Riverside, USA Time series, temporal and spatial processes, point processes
  • Xiaotong Shen, University of Minnesota, USA Learning, Graphical models, Recommenders
  • Ali Shojaie, University of Washington High-dimensional statistics; Statistical learning; graphical models; network analysis
  • Ilya Shpitser, Johns Hopkins University causal inference, missing data, algorithmic fairness, semi-parametric statistics
  • Aarti Singh, Carnegie Mellon University, USA Statistical learning theory, sequential decision making, interactive learning, deep learning theory, statistical signal processing
  • Mahdi Soltanolkotabi,
  • David Sontag, Massachusetts Institute of Technology Graphical models, approximate inference, structured prediction, unsupervised learning, applications to health care
  • Bharath Sriperumbudur, Pennsylvania State University Kernel Methods, Regularization, Theory of Functions and Spaces, Statistical Learning Theory, Nonparametric Estimation and Testing, Functional Data Analysis, Topological Data Analysis
  • 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, sequential decision making, learning theory
  • Jin Tian, Iowa State University causal inference, Bayesian networks, probabilistic graphical models
  • Ivan Titov, University of Edinburgh, UK 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, France kernel methods, computational biology, statistical learning theory
  • Silvia Villa, Genova University, Italy optimization, convex optimization, first order methods, regularization
  • Manfred Warmuth, Google Research
  • Kilian Weinberger, Cornell University, USA Deep Learning, Representation Learning, Ranking, Computer Vision
  • Adrian Weller, University of Cambridge & The Alan Turing Institute, UK Fairness, Interpretability
  • Martha White, University of Alberta reinforcement learning, representation learning
  • Chris Wiggins, Columbia University Computational biology, ethics, bandits
  • Tong Zhang, Hong Kong University of Science and Technology learning theory, optimization, large scale learning
  • Zhihua Zhang, Peking University, China Bayesian Analysis and Computations, Numerical Algebra and Optimization
  • Mingyuan Zhou, The University of Texas at Austin Approximate inference, Bayesian methods, deep generative models, discrete data analysis
  • Ji Zhu, University of Michigan, Ann Arbor Network data analysis, latent variable models, graphical models, high-dimensional data, health analytics.

JMLR-MLOSS Editors

  • Alexandre Gramfort, INRIA, Universit√© Paris-Saclay, France. supervised learning, convex optimization, sparse methods, machine learning software, applications in neuroscience
  • Sebastian Schelter, University of Amsterdam & Apache Software Foundation. Data management for machine learning; data quality; relational data preparation.
  • Joaquin Vanschoren, Eindhoven University of Technology, Netherlands. Automated machine learning, meta-learning, machine learning software.
  • Zeyi Wen, Hong Kong University of Science and Technology (Guangzhou). Machine learning systems, automatic machine learning, kernel methods, decision trees.

JMLR Editorial board of reviewers

The Editorial board of reviewers is a collection of trusted reviewers, which commit to review at least 2 papers per year. Please reach out to us at editor@jmlr.org if you'd like to volunteer to be in this list of trusted reviewers:

  • David Abel, reinforcement learning, planning, abstraction
  • Evrim Acar, matrix/tensor factorizations
  • Maximilian Alber, deep learning, semantic segmentation, software, attribution methods/"explainable ai"
  • Mauricio A Alvarez, Gaussian processes, kernel methods, Bayesian inference, physics-inspired machine learning, data-centric engineering
  • David Alvarez-Melis, Optimal Transport, Optimization, Unsupervised Learning
  • Chris Amato, multi-agent reinforcement learning, partially observable reinforcement learning, multi-robot systems
  • Weihua An, Network Analysis, Causal Inference, Bayesian Analysis, Experiments
  • Rika Antonova, Bayesian optimization, reinforcement learning, variational inference, learning for robotics
  • michael arbel, kernel methods, deep learning
  • Cedric Archambeau, Probabilistic machine learning, approximate inference, variational inference, meta-learning, continual learning, Bayesian deep learning, Bayesian optimisation, hyperparameter optimisation, neural architecture search, AutoML
  • Ery Arias-Castro, clustering, multidimensional scaling, manifold learning, hypothesis testing, multiple testing, nonparametric methods
  • Yossi Arjevani, optimization, lower bound, ReLU models, symmetry
  • Raman Arora, stochastic optimization, subspace learning, differential privacy, robust adversarial learning, algorithmic regularization
  • Devansh Arpit, deep learning, representation learning, optimization, generalization
  • Alexander Aue, time series, high-dimensional statistics, change-points
  • Valeriy Avanesov, learning theory, distributed learning, kernel methods, nonparametric and high-dimensional statistics, bootstrap
  • Kamyar Azizzadenesheli, Learning theory, Reinforcement Learning, Bandit Algorithms
  • Rohit Babbar, Large-scale multi-label learning, Extreme Classification, Imbalanced classification
  • Krishnakumar Balasubramanian, Sampling and stochastic optimization, Kernel methods, Geometric and topological data analysis, Statistical learning theory.
  • Raef Bassily, differential privacy, statistical learning theory, optimization, stochastic gradient descent, generalization guarantees, adaptive data analysis, information theory
  • Gustavo Batista, time series, data streams, class imbalanced, embedded machine learning, quantification
  • Kayhan Batmanghelich, ML for Healthcare, Explainability, Weakly Supervised Learning, Disentanglement, Medical Imaging, Probabilistic Graphical Model
  • Denis Belomestny, MCMC, Variance reduction, deconvolution problems, reinforcement learning
  • Thomas Berrett,
  • Srinadh Bhojanapalli, optimization, deep learning, transformers, non-convex optimization
  • Przemyslaw Biecek, explainable ai, interpretable machine learning, evidence based machine learning, human centered artificial intelligence
  • Michael Biehl, learning vector quantization, prototype based systems, statistical physics of learning, biomedical applications
  • Gilles Blanchard, learning theory, kernel methods, high-dimensional inference, multiple testing, statistics
  • Mathieu Blondel, structured prediction, differentiable programming, optimization
  • Omer Bobrowski, geometric and topological inference, probabilistic modeling, gaussian processes
  • Giorgos Borboudakis, feature selection, causal discovery, automated machine learning, model selection
  • Guy Bresler, complexity of statistical inference, probabilistic models, random graphs, applied probability
  • Peter Bubenik, topological data analysis, applied topology, applied algebra, applied category theory
  • Cody Buntain, social media, text mining, network science, multi-modal learning, information retrieval
  • David Burns, time series learning, human activity recognition, novelty detection, out of distribution detection, open set classification
  • Diana Cai, Bayesian nonparametrics, probabilistic modeling, Bayesian modeling
  • Burak Cakmak, approximate Bayesian inference, message passing, variational Inference
  • Timothy Cannings, Classification, statistical learning, high-dimensional data, data perturbation techniques, nonparametric methods
  • Olivier Capp, Bandit Algorithms, Statistics, Signal Processing
  • Iain Carmichael, Multi-view data, high-dimensional, statistics
  • Luigi Carratino, kernel methods, large-scale, regularization, optimization
  • Antonio Cavalcante Araujo Neto, clustering, unsupervised learning, graphs, density estimation
  • Adam Charles, Signal Processing, Computational Neuroscience, Dictionary learning, deconvolution, Compressed sensing, Inverse problems, Regularizations, Recurrent neural networks
  • Pratik Chaudhari, deep learning, optimization
  • Jianbo Chen, adversarial examples; adversarial robustness; model interpretation; explainability
  • Bo Chen, deep learning, generative model, Bayesian inference,
  • Xi Chen, high-dimensional statistics, stochastic and robust optimization, machine learning for revenue management, crowdsourcing, choice modelling
  • Jie Chen, graph deep learning, Gaussian process, kernel method
  • Yuansi Chen, domain adaptation, MCMC sampling, optimization, computational neuroscience
  • Lin Chen, optimization, machine learning theory
  • Shizhe Chen, point process, graphical model, neuroscience, experimental design
  • Cheng Chen, matrix factorization, optimization, online learning
  • Kun Chen, Integrative statistical learning, dimension reduction, low-rank models, robust estimation, large-scale predictive modeling, healthcare analytics
  • Victor Chernozhukov, causal models, structural equation models, treatment effects, quantile and distributional methods, high-dimensional inference
  • Silvia Chiappa, Causal inference, Approximate Bayesian inference, variational inference, ML fairness
  • David Choi, statistics, network data analysis, stochastic blockmodel
  • Andreas Christmann, kernel methods, robust statistics, support vector machines
  • Delin Chu, scientific computing, data dimensionality reduction, optimization techniques
  • Carlo Ciliberto, kernel methods, statistical learning theory, structured prediction, meta-learning, multi-task learning
  • Nadav Cohen, Machine Learning, Deep Learning, Statistical Learning Theory, Tensor Analysis, Non-Convex Optimization
  • Taco Cohen, deep learning, equivariance, geometry, data compression
  • Lorin Crawford, deep learning, kernel methods, interpretability, Bayesian, computational biology
  • Lehel Csat, probabilistic inference, gaussian processes, non-parametric methods
  • Yifan Cui, causal inference, foundation of statistics, sampling, statistical machine learning, survival analysis
  • James Cussens, graphical models, relational learning
  • Andy Dahl, Genetics, Variance Decomposition, Matrix/Tensor Factorization, Clustering
  • Xiaowu Dai, kernel methods, matching markets, mechanism design, high-dimensional statistics, nonparametric inference, dynamic systems
  • Ben Dai, statistical learning theory, ranking, recommender systems, numerical embedding
  • Andreas Damianou, Gaussian process, transfer learning
  • Jesse Davis, relational learning, PU learning, sports analytics, anomaly detection
  • Cassio de Campos, Probabilistic Circuits, Probabilistic Graphical Models, Imprecise Probability, Credal Models, Computational Complexity, Robustness
  • Chris De Sa, optimization,MCMC,manifolds,systems,parallelism,distributed,decentralized
  • ernesto de vito, kernel methods, mathematical foundation machine learning, reproducing kernel Hilbert spaces
  • Krzysztof Dembczynski, multi-label classification, extreme classification, large-scale learning, learning theory, learning reductions
  • Carlo DEramo, Reinforcement Learning, Deep Learning, Multi-task learning
  • Michal Derezinski, randomized linear algebra, statistical learning theory, determinantal point processes
  • Alexis Derumigny, high-dimensional linear regression, copula models, kernel smoothing
  • Paramveer Dhillon, NLP, Text Mining, Matrix Factorization, Social Network Analysis, Computational Social Science
  • Laxman Dhulipala, parallel graph algorithms, graph embedding, shared-memory graph algorithms, distributed graph algorithms
  • Thomas Dietterich,
  • Edgar Dobriban, statistical learning theory, sketching, distributed learning, dimension reduction, mathematics of deep learning
  • Michele Donini, automl, fairness, kernel methods
  • Christian Donner, bayesian inference, Gaussian process, variational inference, density estimation, nonparametric models
  • Dejing Dou, semantic data mining, deep learning, information extraction, health informatics
  • Kumar Avinava Dubey, Bayesian Inference, Question Answering, Bayesian Nonparametrics, deep learning
  • Sebastijan Dumancic, statistical relational learning, neuro-symbolic methods, inductive logic programming, program induction, probabilistic programming
  • Jack Dunn, optimization,decision trees,interpretability
  • David Duvenaud, deep learning, Gaussian processes, approximate inference, differential equations
  • Yonathan Efroni, Reinforcement Learning, Bandits, Online Learning
  • Dumitru Erhan, deep learning, self-supervised learning, unsupervised learning, domain adaptation, object detection, model understanding
  • Shobeir Fakhraei, Graph Mining, Graph Neural Networks
  • Zhou Fan, random matrices, random graphs, free probability, high-dimensional asymptotics
  • Max Farrell, causal inference, nonparametrics, deep learning, semiparametrics,
  • Moran Feldman, submodular maximization, streaming algorithms, online algorithms, combinatorial optimization
  • Olivier Fercoq, optimization, convex analysis, coordinate descent, primal-dual methods
  • Tamara Fernandez, kernel methods, survival analysis, Gaussian processes, non-parametric statistics
  • Matthias Feurer, Automated Machine Learning, Hyperparameter Optimization, Bayesian Optimization
  • Aaron Fisher, causal inference, interpretable machine learning, wearable device data, matrix decompositions
  • Madalina Fiterau, ensemble methods, deep learning, multimodal learning, medical imaging, health applications
  • Remi Flamary, optimal transport, domain adaptation, optimization
  • Nicolas Flammarion, optimization
  • Seth Flaxman, Bayesian inference, kernel methods, Gaussian processes
  • Michael Fop, Feature selection, Graphical models, High-dimensional data analysis, Model-based clustering and classification, Statistical network analysis
  • Dylan Foster, Reinforcement learning, control, contextual bandits, online learning, statistical learning, optimization, deep learning
  • Jordan Frecon, Hyperparameter optimization, Structured sparsity, Multitask learning, Optimization, Bilevel programming
  • Roy Frostig, statistical learning theory, stochastic optimization, differentiable programming
  • Piotr Fryzlewicz, time series, change-point and change detection, high-dimensional inference, dimension reduction, wavelets, multiscale methods, statistical learning, networks
  • Chad Fulton, time series, bayesian analysis, econometrics
  • Rahul G. Krishnan, deep generative models, unsupervised learning, applications to health care, state space models
  • Xu Gao, time series, deep learning, spatial temporal model
  • Chao Gao, robust statistics, high-dimensional statistics, Bayes theory, network analysis
  • Tingran Gao, kernel methods, manifold learning, topological data analysis
  • Jochen Garcke, kernel methods, manifold learning, interpretability, high-dimensional approximation, uncertainty quantification, numerical simulations
  • Roman Garnett, Gaussian processes, Bayesian optimization, active learning
  • Damien Garreau, statistics, interpretability, statistical learning theory
  • Saeed Ghadimi, nonconvex optimization, stochastic gradient-based algorithms, sample complexity
  • Asish Ghoshal, statistical learning theory, causal inference, graphical models
  • Gauthier Gidel, optimization, deep learning theory, game theory
  • Pieter Gijsbers, AutoML, meta-learning
  • Olivier Goudet, causality, metaheuristics
  • Robert Gower, Stochastic optimization, sketching, randomized numerical linear algebra, linear algebra, quasi-Newton methods, SGD, stochastic gradient descent
  • Navin Goyal, deep learning, learning theory,
  • Roger Grosse, neural net optimization, Bayesian deep learning, hyperparameter adaptation
  • Steffen Grunewalder, statistical learning theory, kernel methods, multi armed bandits
  • Yuwen Gu, high-dimensional statistics, variable selection, nonparametric statistics, model combination, optimization, causal inference
  • Quanquan Gu, convex and nonconvex optimization, theory of deep learning, high-dimensional statistics
  • Benjamin Guedj, Learning theory, PAC-Bayes, computational statistics, high-dimensional statistics, theory of deep learning, probabilistic models, Bayesian inference
  • Ishaan Gulrajani, deep learning, generative modeling, out-of-distribution generalization
  • Tom Gunter, gaussian processes, bayesian nonparametrics, cox processes, bayesian inference
  • Xin Guo, ranking and preference learning, regression, learning theory, supervised learning, semi-supervised learning, online learning, kernel methods, sparsity regularization
  • xin guo, deep learning and Games, reinforcement learning, GANs
  • Minh Ha Quang, kernel methods, statistical learning theory, matrix and operator theory, differential geometrical methods, information geometry, infinite-dimensional statistics
  • Amaury Habrard, Metric Learning, Transfer Learning, Domain Adaptation, Representation learning, statistical learning
  • Jussi Hakanen, optimization, multiobjective optimization, bayesian optimization, kriging, human in the loop
  • William Hamilton, graph representation learning; natural language processing; network analysis
  • Lei Han, reinforcement learning, supervised learning, transfer learning
  • Chulwoo Han, asset pricing, financial application, deep learning
  • Bo Han, deep learning, weakly supervised learning, label-noise learning, adversarial learning
  • Steve Hanneke, learning theory, active learning, sample complexity, PAC learning, VC theory, compression schemes, machine teaching, non-iid learning
  • Ben Hansen, optimal matching, multivariate distance matching, potential-outcomes based causal inference
  • Botao Hao, bandits, reinforcement learning, exploration, tensor methods
  • Ning Hao, Change-point analysis, High-dimensional data, Multivariate analysis, Statistical machine learning.
  • Ethan Harris, MLOSS, deep learning, augmentation, computational neuroscience
  • Mohamed Hebiri, high dimensional statistics, statistical fairness, distribution-free algorithms, minimax theory
  • Reinhard Heckel, deep learning, optimization, active learning
  • Markus Heinonen, Gaussian processes, dynamical models, differential equations, bayesian neural networks, kernel methods
  • James Hensman, gaussian processes, variational inference, biostatistics
  • Daniel Hernndez Lobato, Approximate Inference, Gaussian Processes, Bayesian Optimization
  • Jun-ichiro Hirayama, unsupervised learning, brain imaging, neuroscience, signal processing, independent component analysis
  • Nhat Ho, Statistical learning theory, Optimal transport, Bayesian nonparametrics, Bayesian inference, Mixture and hierarchical models, Optimization, Deep generative models, Variational inference
  • Jean Honorio, learning theory, planted models, graphical models, structured prediction, community detection
  • Giles Hooker, random forrests, intelligibility, explanations, confidence intervals, uncertainty quantification, hypothesis tests, variable importance, central limit theorems
  • Thibaut Horel, optimization, convex analysis, game theory, diffusion processes
  • Tamas Horvath, pattern mining, graph mining, relational learning, inductive logic programming, learning from structured data, networks
  • Torsten Hothorn, statistical learning
  • Daniel Hsu, Learning theory
  • Wei Hu, deep learning theory
  • Jianhua Huang, statistical machine learning, dimension reduction, statistical inference, Bayesian optimization, transfer learning
  • Nicolas Hug, open source, gradient boosting, python, software
  • Jonathan Huggins, Bayesian methods, Bayesian computation, kernel methods, robust inference, large-scale learning
  • Eyke Hllermeier, preference learning and ranking, uncertainty in machine learning, multi-target prediction, weakly supervised learning, learning on data streams
  • Masaaki Imaizumi, statistics, learning theory, tensor, functional data, deep learning theory
  • Rishabh Iyer, submodular optimization, active learning, compute efficient learning, robust learning, data subset selection, data summarization
  • Kevin Jamieson, Multi-armed bandits, active learning, experimental design
  • Lucas Janson, high-dimensional inference, variable importance, reinforcement learning
  • Ghassen Jerfel, bayesian machine learning, statistical inference, uncertainty, unsupervised learning, sampling, MCMC, optimization
  • Sean Jewell, changepoint detection, selective inference, neuroscience
  • Lu Jiang, robust deep learning, curriculum learning, multimodal learning
  • Nan Jiang, reinforcement learning theory
  • Heinrich Jiang, fairness, data labeling, clustering
  • Chi Jin, nonconvex optimization, reinforcement learning theory
  • julie josse, missing values, causal inference, matrix completion
  • Varun Kanade, computational learning theory, online learning, clustering, deep learning (theory), optimization
  • Motonobu Kanagawa, kernel methods, simulation models, uncertainty quantification
  • shiva Kasiviswanathan, Privacy, Learning Theory, Optimization, Algorithms
  • Emilie Kaufmann, multi-armed bandit, reinforcement learning
  • Kshitij Khare, Graphical models, Bayesian computation, Vector autoregressive models
  • Rahul Kidambi, stochastic optimization, stochastic gradient descent, optimization, offline reinforcement learning, model-based reinforcement learning, batch learning with bandit feedback, offline contextual bandit learning
  • Yoon Kim, natural language processing, deep learning
  • Pieter-Jan Kindermans, interpretability, explainability, understanding neural networks, deep learning, neural architecture search, brain machine interfaces, brain computer interfaces
  • Johannes Kirschner, bandits, Bayesian optimization, partial monitoring
  • Arto Klami, probabilistic models, variational inference, matrix factorization, canonical correlation analysis
  • Aaron Klein, Bayesian optimization, AutoML, neural architecture search
  • Jason Klusowski, Deep learning, neural networks, decision tree learning
  • Murat Kocaoglu, causal inference, information theory, deep generative models
  • Mladen Kolar, high-dimensional statistics, graphical models
  • Dehan Kong, kernel methods, matrix and tensor methods, causal inference, high dimensional inference, manifold learning, robust methods, neuroimaging and genetics
  • Jean Kossaifi, deep learning, tensor methods
  • Sanmi Koyejo, federated learning, distributed machine learning, robust machine learning, statistical learning theory, neuroimaging, machine learning for medical imaging, machine learning for healthcare
  • Akshay Krishnamurthy, statistical learning theory, reinforcement learning, bandits
  • Todd Kuffner, statistics, post-selection inference, resampling, bootstrap, asymptotics, testing
  • Vitaly Kuznetsov, time series, learning theory, quantitative finance
  • Branislav Kveton, bandits, online learning, reinforcement learning
  • Jakub Lacki, graph algorithms, clustering, distributed optimization
  • Vincenzo Lagani, Causal analysis, bioinformatics
  • Silvio Lattanzi, clustering, graph mining, submodular optimization
  • Tor Lattimore, Bandits, reinforcement learning, online learning
  • Rmi LE PRIOL, deep learning, optimization, duality
  • Nicolas Le Roux, optimization, reinforcement learning
  • Johannes Lederer, deep learning theory, high-dimensional statistics
  • Sokbae Lee, econometrics, causal inference, quantile regression, mixed integer optimization
  • Holden Lee, MCMC, sampling algorithm, control theory, reinforcement learning
  • Yoonkyung Lee, Kernel methods, ranking, loss functions, dimension reduction
  • Guillaume Lemaitre, software engineering, open source software, class imbalance
  • Yujia Li, deep learning, graph neural networks, program synthesis, program induction
  • Hao Li, deep learning, vision, generative models, optimization
  • Shuai Li, Machine intelligence, online prediction, decision making, bandits, learning theory, optimization
  • Didong Li, Nonparametric Bayes, geometric data analysis, manifold learning, information geometry, spatial statistics
  • Xiaodong Li, matrix completion, network analysis, optimization
  • Tianyang Li, optimization, statistics, machine learning, stochastic optimization, statistical inference, high dimensional statistics, robust learning
  • Yi Li, sparse recovery, randomized numerical linear algebra
  • Heng Lian, statistics, learning theory, reproducing kernel Hilbert space, distributed optimization
  • Tengyuan Liang, deep learning theory, kernel methods, interpolation, high-dimensional asymptotics
  • Wei Lin, high-dimensional statistics, statistical machine learning, causal inference
  • Tianyi Lin, min-max optimization, optimal transport
  • Hongzhou Lin, optimization
  • Wu Lin, Variational Inference, Stochastic Optimization
  • Marius Lindauer, automated machine learning, hyperparameter optimization, neural architecture search
  • Zachary Lipton, deep learning, healthcare, natural language processing, robustness, causality, fairness, technology and society
  • Weidong Liu, statistical optimization, Gaussian graphical model, precision matrix,false discovery rate
  • Liping Liu, generative models, graph neural networks, self-attention models
  • Chong Liu, ensemble methods, differential privacy, active learning, crowdsourcing
  • Yang Liu, learning with noisy data, weakly supervised learning, crowdsourcing
  • Tongliang Liu,
  • SONG LIU, density ratio estimation, graphical model, stein indentity, change detection, outlier detection
  • Karen Livescu, representation learning, multi-view learning, speech processing, natural language processing, sign language
  • Galle Loosli, kernel methods, indefinite kernels, adversarial robustness
  • Miles Lopes, bootstrap methods, high-dimensional statistics, sketching algorithms (error analysis of)
  • Qi Lou, graphical models, computational advertising
  • Bryan Kian Hsiang Low, Gaussian process, Bayesian optimization, active learning, automated machine learning, probabilistic machine learning, data valuation, fairness in collaborative/federated learning
  • Daniel Lowd, adversarial machine learning, statistical relational learning, Markov logic, tractable probabilistic models, sum-product networks, probabilistic graphical models, Markov networks, Bayesian networks, Markov random fields
  • Aurelie Lozano, high-dimensional estimation, deep learning, optimization
  • Haihao Lu, optimization
  • Junwei Lu, high dimensional statistics
  • Aurelien Lucchi, optimization, deep learning theory
  • Haipeng Luo, online learning, bandit problems, reinforcement learning
  • Luo Luo, optimization, numerical linear algebra
  • Yuetian Luo, tensor data analysis, statistical and computational trade off
  • Eric Ma, network science, graph theory, applied deep learning, applied bayesian statistics
  • Siyuan Ma, optimization, kernel methods, deep learning
  • Zongming Ma, statistics, optimality, social network
  • Yi-An Ma, Bayesian inference, time series analysis
  • Shujie Ma, deep learning, causal inference, network analysis, nonparametric methods, dimensionality reduction, time series data
  • Tengyu Ma, deep learning theory
  • Qing Mai, High-dimensional data analysis, Tensor data analysis, Machine learning, Semiparametric and nonparametric statistics, Dimension reduction
  • odalric-ambrym maillard, multi-armed bandits, reinforcement learning, markov decision processes, concentration of measure
  • Man Wai Mak, Speaker recognition, deep learning, domain adaptation, noise robustness, ECG classification
  • Ameesh Makadia, 3D computer vision, geometric deep learning, harmonic analysis
  • Gustavo Malkomes, Bayesian optimization, active learning, Gaussian processes, active model selection
  • Stephan Mandt, variational inference, deep latent variable models, machine learning and physics, neural data compression
  • Horia Mania, Reinforcement Learning, control theory
  • Timothy Mann, reinforcement learning, optimization, robustness, transfer learning, delay
  • Rahul Mazumder, mathematical optimization, high-dimensional statistics, sparsity, Boosting, nonparametric statistics, shape constrained estimation
  • Julian McAuley, personalization, recommender systems, web mining
  • Daniel McDonald, statistical machine learning, high-dimensional statistics, time series, optimization, risk estimation
  • Song Mei, deep learning, kernel methods
  • gonzalo mena, optimal transport, statistics, computational biology
  • Lucas Mentch, Random Forests, Ensembles, Explainability, Variable Importance, Hypothesis Testing
  • bjoern menze, random forest, deep learning, biomedical, imaging
  • Bertrand Michel, Model selection, topological data analysis, unsupervised learning
  • Andrew Miller, statistical inference, health, Gaussian processes, MCMC, variational inference
  • Ezra Miller, geometry, algebra, combinatorics, topology, geometric and topological data analysis, evolutionary biology
  • Bamdev Mishra, Riemannian optimization, manifold optimization, matrix tensor decompositions, stochastic algorithms
  • Ioannis Mitliagkas, optimization, theory of deep learning, large scale learning, minimax optimization
  • Alejandro Moreo Fernndez, deep learning, text classification, domain adaptation, word embeddings, kernel methods, transfer learning
  • Dmitriy Morozov, topological data analysis
  • Nicole Mcke, kernel methods, stochastic approximation, deep learning, (de-)centralized learning, regularization methods, inverse problems, learning theory
  • Shinichi Nakajima, Bayesian learning, variational inference, generative model
  • Eric Nalisnick, bayesian methods, deep learning, approximate inference, generative models, out-of-distribution detection
  • Preetam Nandy, Causal Inference, Causal Structure Learning, Graphical Models, High-dimensional Data, Reinforcement Learning, Fairness in Machine Learning,
  • Harikrishna Narasimhan, Evaluation Metrics, Constrained Optimization, Fairness, Learning Theory, Convex Optimization
  • Ion Necoara, convex optimization, stochastic optimization, kernel methods, supervised learning
  • Gergely Neu, reinforcement learning, learning theory, online learning, bandit theory
  • Gerhard Neumann, reinforcement learning, policy search, deep learning, robotics,
  • Behnam Neyshabur, deep learning, learning theory, generalization
  • Vlad Niculae, structured prediction, optimization, argmin differentiation, natural language processing
  • Yang Ning, high dimensional statistics, statistical inference, causal inference
  • Jose Nino-Mora, optimization, probabilistic models, bandit problems
  • Atsushi Nitanda, stochastic optimization, statistical learning theory, deep learning, kernel methods
  • David Nott, Bayesian model choice and model criticism, likelihood-free inference, variational inference
  • Alex Nowak-Vila, kernel methods, structured prediction, inverse problems, statistical learning theory, convex optimization
  • Aidan O'Brien, bioinformatics, feature selection, implementation
  • Kevin O'Connor, optimal transport, inference for dynamical systems
  • Ronald Ortner, reinforcement learning
  • Satoshi Oyama, kernel methods, link prediction, crowdsourcing
  • Ana Ozaki, exact learning, pac learning, neural network verification, logic, ontologies, knowledge graphs
  • Randy Paffenroth, deep learning, theory of machine learning, unsupervised learning, applied mathematics
  • Amichai Painsky, Statistics, Information Theory, Statistical Inference, Predictive Modeling, Tree-based Models, Data Compression, Probability Estimation
  • Evangelos Papalexakis, factorization methods, tensor factorization, tensor decomposition, matrix factorization, matrix decomposition, unsupervised methods
  • Laetitia Papaxanthos, Deep learning, data mining, computational biology
  • Biswajit Paria, bayesian optimization
  • Gunwoong Park, directed acyclic graphical models, causal inference
  • CHANGYI PARK, Kernel methods, support vector machines, feature selection
  • Matt Parry, probabilistic modelling, scoring rules, Bayesian statistics
  • Razvan Pascanu, deep learning, optimization, reinforcement learning, continual learning, graphnetes
  • Jose M Pena, causality, probabilistic graphical models
  • Richard Peng, graph algorithms, optimization, numerical methods
  • Will Perkins, probability, statistical physics, combinatorics, random graphs
  • Victor Picheny, Bayesian optimization, Gaussian process
  • Brad Price, Statistical Machine Learning, Multivariate and Multi-Task Methods, Graph Constrained Models
  • Yumou Qiu, High-dimensional statistical inference, Gaussian graphic model, kernel smoothing, Statistical analysis for brain imaging, causal inference, High-throughput plant phenotying
  • Yixuan Qiu, statistical computing, optimization, MCMC
  • Qing Qu, nonconvex optimization, representation learning, inverse problems, unsupervised learning
  • Peter Radchenko, high-dimensional statistics, sparse learning and estimation, feature selection.
  • Manish Raghavan, algorithmic fairness, game theory, behavioral economics
  • Maxim Raginsky, Theory of deep learning, statistical learning, optimization, applied probability, concentration of measure, dynamical systems and control
  • Anand Rajagopalan, Clustering, Random Matrix Theory
  • Goutham Rajendran, Machine Learning Theory, Generative Models, Representation Learning, Latent Variable Models, Variational Autoencoders, Causal Inference, Graph Neural Networks
  • Herilalaina Rakotoarison, automl, algorithm selection
  • Jan Ramon, privacy preserving learning, learning from graphs, learning theory
  • Rajesh Ranganath, Approximate Inference, Deep Generative Models, Causal Inference, Machine Learning
  • Vinayak Rao, Markov Chain Monte Carlo, Monte Carlo, Bayesian methods, Bayesian nonparametrics, Variational Inference, Point Processes, Stochastic Processes
  • Jesse Read, multi-label, multi-output, data streams
  • Zhao Ren, high-dimensional statistics, robust statistics, graphical models
  • Steffen Rendle, recommender systems,large scale learning,matrix factorization
  • Marcello Restelli, reinforcement learning
  • Lev Reyzin, learning theory, graph algorithms, ensemble methods, bandits
  • Bruno Ribeiro, relational learning, invariant representations, embeddings, graph neural networks
  • Bastian Rieck, topological data analysis, computational topology, kernel methods, networks and graphs, applications to healthcare
  • Fabrizio Riguzzi, relational learning, statistical relational learning, inductive logic programming, probabilistic inductive logic programming
  • Omar Rivasplata, Statistical Learning Theory, PAC-Bayes bounds, deep learning, mathematics, probability and statistics
  • Ariel Rokem, Neuroinformatics, regularized regression, open-source software, data science
  • Alessandro Rudi, Kernel methods, statistical machine learning
  • Sivan Sabato, statistical learning theory, active learning, interactive learning
  • Veeranjaneyulu Sadhanala,
  • Saverio Salzo, Convex optimization, kernel methods
  • JEROME SARACCO, dimension reduction, nonparametric and semiparametric regression, clustering, non parametric conditional quantile estimation
  • Hiroaki Sasaki, unsupervised learning, density estimation, kernel methods
  • Kevin Scaman, optimization, distributed optimization
  • Florian Schaefer, optimization, game theory, GANs, Gaussian processes
  • Mikkel Schmidt, Approximate Bayesian inference, Probabilistic modeling, Markov chain Monte Carlo, Variational Inference, Bayesian nonparametrics, Network data analysis, Reinforcement learning, Generative models, Deep learning, Program induction
  • Jacob Schreiber, deep learning, genomics, submodular optimization, tensor factorization, imputation
  • Alex Schwing, deep learning, structured prediction, generative adversarial nets
  • Clayton Scott, statistical learning theory, kernel methods, domain adaptation, weak supervision, kernel density estimation, label noise, domain generalization
  • Dino Sejdinovic, kernel methods
  • Yevgeny Seldin, Bandits, PAC-Bayesian Analysis, Online Learning, Learning Theory
  • Rajat Sen, bandit algorithms, online learning, time series
  • Amina Shabbeer, Optimization, deep learning, bioinformatics, natural language processing, reinforcement learning
  • Uri Shalit, causal inference, machine learning in healthcare
  • yanyao shen, optimization, robust learning, large-scale learning
  • Seung Jun Shin, kernel methods, dimension reduction, regularized estimation
  • Ali Shojaie, High-dimensional statistics; Statistical learning; graphical models; network analysis
  • Ilya Shpitser, causal inference, missing data, algorithmic fairness, semi-parametric statistics
  • Si Si, model compression; kernel methods
  • Ricardo Silva, causality, graphical models, Bayesian inference, variational methods
  • Max Simchowitz, control theory, reinforcement learning, bandits
  • Riley Simmons-Edler, Reinforcement Learning, Deep Reinforcement Learning, Exploration
  • Dejan Slepcev, graph based learning, optimal transportation, geometric data analysis, semi-supervised learning, PDE and variational methods
  • Aleksandrs Slivkins, multi-armed bandits, exploration, economics and computation, mechanism design
  • Marek Smieja, deep learning, semi-supervised learning, missing data, anomaly detection, clustering, multi-label classification
  • Arno Solin, Probabilistic modelling, stochastic differential equations, state space models, Gaussian processes, approximative inference
  • Hyebin Song, statistical learning, high dimensional statistics, computational biology, optimization
  • Karthik Sridharan, online learning, learning theory, stochastic optimization
  • Sanvesh Srivastava, Distributed Bayesian inference, Divide-and-Conquer, Gaussian process, latent variable models, Wasserstein barycenter
  • Francesco C. Stingo, biostatistics, Bayesian analysis, model selection, graphical models
  • Karl Stratos, representation learning, information theory, spectral methods, natural language processing
  • Weijie Su, statistics, differential privacy, optimization
  • Mahito Sugiyama, clustering, feature selection, pattern mining, graph mining
  • Yanan Sui, AI Safety, Bandit, Bayesian Optimization, Medical Application
  • Ruoyu Sun, optimization, deep learning
  • Shiliang Sun, Probabilistic Model and Approximate Inference, Optimization, Statistical Learning Theory, Multi-view Learning, Trustworthy Artificial Intelligence, Sequential Data Modeling
  • Taiji Suzuki, kernel methods, deep learning, optimization
  • Zoltan Szabo, information theory, kernel techniques
  • Ronen Talmon, kernel methods, manifold learning, geometric methods, spectral graph theory
  • Vincent Tan,
  • Kean Ming Tan, graphical models, unsupervised learning, low rank approximation
  • Cheng Yong Tang, covariance modeling, graphical models, high-dimensional statistical learning, nonparametric methods, statistical inference
  • Wesley Tansey, Bayesian statistics, empirical Bayes, graphical models, computational biology, hypothesis testing
  • Chen Tessler, deep reinforcement learning, reinforcement learning
  • Albert Thomas, machine learning software, python, anomaly detection
  • Jin Tian, causal inference, Bayesian networks, probabilistic graphical models
  • Felipe Tobar, Gaussian processes, Bayesian inference, Bayesian nonparametrics, Time Series
  • Kim-Chuan Toh, convex optimization, sparse Newton methods, semidefinite programming, polynomial optimization
  • Panos Toulis, causal inference, randomization tests, stochastic gradient, stochastic approximations, networks
  • Sofia Triantafillou, causality; probabilistic graphical models; Bayesian networks
  • Ivor Tsang, Transfer Learning, Kernel Methods, Deep Generative Models, Weakly Supervised Learning, Imitation Learning
  • Cesar A. Uribe, optimization, decentralized optimization, optimal transport, distributed optimization, social learning, network science
  • Inigo Urteaga, Bayesian Theory, generative models, approximate inference, Bayesian nonparametrics, multi-armed bandits
  • Ewout van den Berg, convex optimization, quantum computing
  • Laurens van der Maaten, computer vision, privacy
  • Stfan van der Walt, open source software, image processing, array computing
  • Jan N van Rijn, Machine Learning, AutoML, Automated Design of Algorithms, meta-learning
  • Bart Vandereycken, Riemannian optimization, manifold methods, low-rank approximation, tensor decomposition, numerical linear algebra
  • Gael Varoquaux, dirty data, missing values, brain imaging, healthcare
  • Kush Varshney, fairness, interpretability, safety, applications to social good
  • Aki Vehtari, Bayesian analysis, Bayesian statistics, Gaussian processes
  • Silvia Villa, optimization, convex optimization, first order methods, regularization
  • Max Vladymyrov, non-convex optimization, manifold learning, neural architecture search
  • Zi Wang, robot learning, Bayesian optimization, learning and planning, Gaussian process, active learning
  • Yu-Xiang Wang, statistical machine learning, optimization, differential privacy, reinforcement learning
  • Chien-Chih Wang, optimization, deep learning, large-scale classification
  • Lan Wang, high-dimensional statistics, optimal decision estimation, nonparametric and semiparametric statistics, quantile regression, causal inference
  • Yuhao Wang, causal inference, high-dimensional statistics, semiparametric inference, graphical models
  • Mengdi Wang, reinforcement learning, representation learning
  • Serena Wang, fairness, constrained optimization, robust optimization, ensemble methods
  • Weiran Wang, representation learning, deep learning, speech processing, sequence learning
  • Chong Wang, approximate inference, deep learning, uncertainty, generative models
  • Jialei Wang, optimization, high-dimensional statistics, learning theory
  • Xiaoqian Wang, explainable AI, fairness in machine learning, generative model
  • Y. Samuel Wang, Graphical Models, Causal Discovery
  • Zhaoran Wang, reinforcement learning
  • Kazuho Watanabe, latent variable models, rate-distortion theory
  • Andrew Gordon Wilson, Bayesian deep learning, Gaussian processes, generalization in deep learning
  • Ole Winther, deep learning, generative models, gaussian processes
  • Guy Wolf, manifold learning, geometric deep learning, data exploration
  • Raymond K. W. Wong, nonparametric regression, functional data analysis, low-rank modeling, tensor estimation
  • Chirayu Wongchokprasitti, recommender systems, user modeling, causal discovery
  • Jiajun Wu, computer vision, deep learning, cognitive science
  • Yao Xie, statistical learning, spatio-temporal data modeling, sequential analysis, change-point detection, dynamic networks.
  • Lingzhou Xue, high-dimensional statistics, graphical models, dimension reduction, optimization
  • Yuhong Yang, bandit problems, forecasting, model selection and assessment, minimax learning theory
  • Zhirong Yang, dimensionality reduction, cluster analysis, visualization
  • Zhuoran Yang, reinforcement learning, statistical machine learning, optimization
  • Han-Jia Ye, representation learning, meta-learning
  • Felix X.-F. Ye, Model Reduction, Dynamical system, Data-driven modeling
  • Junming Yin, statistical machine learning, probabilistic modeling and inference, nonparametric statistics
  • Yiming Ying, statistical learning theory, optimization in machine learning, kernel methods, differential privacy
  • Rose Yu, deep learning, time series, tensor methods
  • Guo Yu, sparsity; convex optimization; Gaussian graphical models; multiple testing
  • Yaoliang Yu, generative models, optimization, robustness
  • Yi Yu, statistical network analysis, change point detection, high-dimensional statistics
  • Xiaotong Yuan, sparse learning, optimization, meta-learning, non-convex optimization, learning theory, distributed optimization
  • Luca Zanetti, Graph clustering, Markov chains, Spectral methods
  • Assaf Zeevi,
  • Kun Zhang, causality, transfer learning, kernel methods, unsupervised deep learning
  • Jingzhao Zhang, optimization
  • Xinhua Zhang, kernel methods, transfer learning, adversarial learning, representation learning
  • Lijun Zhang, Online learning, Bandits, stochastic optimization, Randomized algorithms
  • Chiyuan Zhang, deep learning
  • Xin Zhang, Dimension Reduction, Multivariate Analysis and Regression, Tensor Data Analysis, Discriminant Analysis, Neuroimaging
  • Michael Minyi Zhang, Bayesian non-parametrics, MCMC, Gaussian processes
  • Aonan Zhang, bayesian methods, bayesian nonparametric, deep unsupervised learning, uncertainty estimation
  • Tuo Zhao, deep learning, nonconvex optimization, high dimensional statistics, natural language processing, open-source software library
  • Zhigen Zhao, high dimensional statistical inference, empirical Bayesian/Bayesian statistics, Sufficient dimension reduction, multiple comparison
  • Han Zhao, domain adaptation, domain generalization, transfer learning, probabilistic circuits, algorithmic fairness, multitask learning, meta-learning
  • Yunpeng Zhao, Network analysis; Community detection
  • Qinqing Zheng, optimization, differential privacy
  • Ping-Shou Zhong, kernel methods, statistical inference, high dimensional data, functional data, nonparametric methods, and genomics and genetics
  • Wenda Zhou, statistical machine learning, deep learning, high-dimensional statistics
  • Zhengyuan Zhou, contexutal bandits, online learning, game theory
  • Shuchang Zhou, optimization,neural network,quantization
  • Ding-Xuan Zhou, deep learning, approximation by deep neural networks, kernel methods, wavelets
  • Ruoqing Zhu, random forests, personalized medicine, survival analysis
  • Liping Zhu, massive data analysis, nonlinear dependence, dimension reduction
  • Marinka Zitnik, representation learning, embeddings, graph neural networks, knowledge graphs, latent variable models, biomedical data, computational biology, network science

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
  • 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