JMLR Editorial Board

Editors-in-Chief

Pradeep Ravikumar, Carnegie Mellon University.
Tong Zhang, University of Illinois Urbana-Champaign.

Managing Editors

Rajarshi Das, University of Massachusetts Amherst.
Hanze Dong, Salesforce Research.

Editorial Assistant

Stella Lianou, Columbia University.

Production Editor

Kevin Bello, Carnegie Mellon University and University of Chicago.

Web Master

Fabian Pedregosa, Google Research.

JMLR Action Editors

Alekh Agarwal, Google Research, USA. Reinforcement Learning, Online Learning, Bandits, Learning Theory.
Edo Airoldi, Harvard University, USA Statistics, approximate inference, causal inference, network data analysis, computational biology.
Genevera Allen, Columbia University 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, Stochastic Optimization, Learning theory.
Arindam Banerjee, UIUC bandits, generative models, deep learning, optimization, learning theory, federated learning
Elias Bareinboim, Columbia University causal Inference, generalizability, fairness, reinforcement learning
Samy Bengio, Apple, USA Deep learning, representation learning
Yoshua Bengio, University of Montreal, Canada / Mila Deep learning, learning to reason
Quentin Berthet, Google DeepMind Convex optimization, optimal transport, differentiable programming, high-dimensional statistics
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
Kai-Wei Chang,
Silvia Chiappa, DeepMind Causal inference, Approximate Bayesian inference, variational inference, ML fairness
Kyle Cranmer, University of Wisconsin-Madison AI/ML for Science, Probabilistic ML, Approximate Inference, Geometric Deep Learning
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
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
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
Quanquan Gu, UCLA optimization, theory of deep learning, reinforcement learning, LLMs, deep generative models, high-dimensional statistics
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
Matthew Hoffman, Google Bayesian inference, Markov chain Monte Carlo, Sequential Monte Carlo,Variational inference
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
Prateek Jain, Microsoft Research, India Non-convex Optimization, Stochastic Optimization, Large-scale Optimization, Resource-constrained Machine Learning
Kevin Jamieson, University of Washington Multi-armed bandits, active learning, experimental design
Nan Jiang, University of Illinois Urbana-Champaign reinforcement learning theory
Varun Kanade, University of Oxford learning theory; online learning; computational complexity; 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 Southern California, 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
Wouter Koolen, CWI, Amsterdam Online Learning, Bandits, Pure Exploration, e-values
Alp Kucukelbir, Columbia University & Fero Labs variational inference, statistical machine learning, approximate inference, diffusion, probabilistic programming
Brian Kulis, Boston University Deep Learning, Clustering, Kernel Methods, Metric Learning, Self-Supervised Learning, Audio Applications, Vision Applications
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
Christoph Lampert, Institute of Science and Technology, Austria (IST Austria) transfer learning, trustworthy learning, computer vision
Tor Lattimore, DeepMind Bandits, reinforcement learning, online learning
Anthony Lee, University of Bristol Markov chain Monte Carlo, sequential Monte Carlo
Honglak Lee, Google and University of Michigan, Ann Arbor Deep Learning, Deep Generative Models, Representation Learning, Reinforcement Learning, Unsupervised Learning
Qiang Liu, Dartmouth College, USA Probablistic graphical models, inference and learning, computational models for crowdsourcing
Jianfeng Lu, Duke University Monte Carlo sampling, scientific machine learning, generative models
Gabor Lugosi, Pompeu Fabra University, Spain statistical learning theory, online prediction, concentration inequalities
Shiqian Ma, Rice University first-order methods, stochastic algorithms, bilevel optimization, Riemannian optimization
Michael Mahoney, University of California at Berkeley, USA randomized linear algebra; stochastic optimization; neural networks; matrix algorithms; graph algorithms; scientific machine learning
Stephan Mandt, variational inference, deep latent variable models, machine learning and physics, neural data compression
Vikash Mansinghka, Massachusetts Institute of Technology, USA Probabilistic programming, Bayesian structure learning, large-scale sequential Monte Carlo
Rahul Mazumder, Massachusetts Institute of Technology mathematical optimization, high-dimensional statistics, sparsity, Boosting, nonparametric statistics, shape constrained estimation, decision tree ensembles, compressing large neural networks
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)
Sayan Mukherjee, Duke University, USA; University of Leipzig; Max Planck Institute for Mathematics in the Sciences Bayesian, Time series, Geometry, Topology, Deep 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
Chris Oates, Newcastle University Bayesian computation, kernel methods, uncertainty quantification
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
Massimiliano Pontil, Istituto Italiano di Tecnologia (Italy), University College London (UK) Multitask and transfer learning, convex optimization, kernel methods, sparsity regularization
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
Peter Richtarik, King Abdullah University of Science and Technology (KAUST) convex and nonconvex optimization, stochastic zero, first and second-order methods, distributed training, federated learning, communication compression, operator splitting, efficient ML
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
Sivan Sabato, Ben Gurion University of the Negev statistical learning theory, active learning, interactive learning
Ruslan Salakhutdinov, Carnegie Mellon University Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization.
Joseph Salmon, Inria High-dimensional statistics, convex optimization, crowdsourcing
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
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
Ingo Steinwart, University of Stuttgart, Germany Statistical learning theory, Kernel-based learning methods (support vector machines), Cluster Analysis, Loss functions
Weijie Su, University of Pennsylvania Differential privacy, deep learning theory, LLMs, high-dimensional statistics, optimization
Csaba Szepesvari, University of Alberta, Canada reinforcement learning, sequential decision making, learning theory
Ambuj Tewari, University of Michigan, USA learning theory, online learning, bandit problems, reinforcement learning, optimization, high-dimensional statistics
Jin Tian, Mohamed bin Zayed University of Artificial Intelligence causal inference, Bayesian networks, probabilistic graphical models
Koji Tsuda, National Institute of Advanced Industrial Science and Technology, Japan.
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
Martha White, University of Alberta reinforcement learning, representation 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
Zhengyuan Zhou, contexutal bandits, online learning, game theory
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, Meta AI. 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.
Albert Bifet, Télécom ParisTech & University of Waikato. Artificial Intelligence, Big Data Science, and Machine Learning for Data Streams.

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, Uncertainty quantification, approximate inference, variational inference. Bayesian, optimisation, hyperparameter optimisation, AutoML. Transfer learning, continual learning. Responsible AI.
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, Stochastic Optimization, Learning theory.
Raef Bassily, differential privacy, privacy-preserving machine learning, learning theory, optimization
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
Francisco Maria Calisto, Human-Computer Interaction, Health Informatics, Breast Cancer, User-Centered Design, Artificial Intelligence, Medical Imaging
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
Bo Chen, deep learning, generative model, Bayesian inference,
Shizhe Chen, point process, graphical model, neuroscience, experimental design
Kun Chen, Integrative statistical learning, dimension reduction, low-rank models, robust estimation, large-scale predictive modeling, healthcare analytics
Xi Chen, high-dimensional statistics, stochastic and robust optimization, machine learning for revenue management, crowdsourcing, choice modelling
Jianbo Chen, adversarial examples; adversarial robustness; model interpretation; explainability
Cheng Chen, matrix sketching, optimization, online learning
Jie Chen, graph deep learning, Gaussian process, kernel method
Yuansi Chen, domain adaptation, MCMC sampling, optimization, computational neuroscience
Lin Chen, optimization, machine learning theory
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
Ben Dai, statistical learning theory, ranking, word embedding
Xiaowu Dai, kernel methods, matching markets, mechanism design, high-dimensional statistics, nonparametric inference, dynamic systems
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, matrix sketching, stochastic optimization, 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
Subhajit Dutta,
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
Yang Feng, machine learning, variable selection, community detection
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, statistical inference, online hypothesis testing, kernel methods
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
Chao Gao, robust statistics, high-dimensional statistics, Bayes theory, network analysis
Tingran Gao, kernel methods, manifold learning, topological data analysis
Xu Gao, time series, deep learning, spatial temporal model
Jochen Garcke, kernel methods, manifold learning, interpretability, high-dimensional approximation, uncertainty quantification, numerical simulations
Roman Garnett, Gaussian processes, Bayesian optimization, active learning
Damien Garreau, interpretability, statistical learning theory, kernel methods
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, optimization, theory of deep learning, reinforcement learning, LLMs, deep generative models, 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
Chulwoo Han, asset pricing, financial application, deep learning
Lei Han, reinforcement learning, supervised learning, transfer 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
Ning Hao, Change-point analysis, High-dimensional data, Multivariate analysis, Statistical machine learning.
Botao Hao, bandits, reinforcement learning, exploration, tensor methods
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
Ameya D. Jagtap, Supervised Learning, Neural Deep Neural Networks, Scientific Machine Learning, Physics-Informed Machine Learning, Transfer Learning, Active Learning, Activation Functions, Distributed Learning, Neural Operator Networks, Graph Neural Networks, Data-Driven Techniques, and Supervised Learning.
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
Heinrich Jiang, fairness, data labeling, clustering
Nan Jiang, reinforcement learning theory
Lu Jiang, robust deep learning, curriculum learning, multimodal learning
Chi Jin, nonconvex optimization, reinforcement learning theory
julie josse, missing values, causal inference, matrix completion
Varun Kanade, learning theory; online learning; computational complexity; 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
Holden Lee, MCMC, sampling algorithm, control theory, reinforcement learning
Sokbae Lee, econometrics, causal inference, quantile regression, mixed integer optimization
Yoonkyung Lee, Kernel methods, ranking, loss functions, dimension reduction
Guillaume Lemaitre, software engineering, open source software, class imbalance
Yi Li, sparse recovery, randomized numerical linear algebra
Didong Li, Nonparametric Bayes, geometric data analysis, manifold learning, information geometry, spatial statistics
Tianyang Li, optimization, statistics, machine learning, stochastic optimization, statistical inference, high dimensional statistics, robust learning
Hao Li, deep learning, vision, generative models, optimization
Yujia Li, deep learning, graph neural networks, program synthesis, program induction
Xiaodong Li, matrix completion, network analysis, optimization
Shuai Li, Machine intelligence, online prediction, decision making, bandits, learning theory, optimization
Heng Lian, statistics, learning theory, reproducing kernel Hilbert space, distributed optimization
Tengyuan Liang, deep learning theory, kernel methods, interpolation, high-dimensional asymptotics
Tianyi Lin, optimization, game theory, optimal transport, networks
Wu Lin, Variational Inference, Stochastic Optimization
Hongzhou Lin, optimization
Wei Lin, high-dimensional statistics, statistical machine learning, causal inference
Marius Lindauer, automated machine learning, hyperparameter optimization, neural architecture search
Zachary Lipton, deep learning, healthcare, natural language processing, robustness, causality, fairness, technology and society
Chong Liu, Bayesian Optimization, Bandits, Active Learning, AI for Science
SONG LIU, density ratio estimation, graphical model, stein indentity, change detection, outlier detection
Yang Liu, learning with noisy data, weakly supervised learning, crowdsourcing
Liping Liu, generative models, graph neural networks, self-attention models
Weidong Liu, statistical optimization, Gaussian graphical model, precision matrix,false discovery rate
Tongliang Liu,
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
Junwei Lu, high dimensional statistics
Haihao Lu, optimization
Aurelien Lucchi, optimization, deep learning theory
Haipeng Luo, online learning, bandit problems, reinforcement learning
Yuetian Luo, tensor data analysis, statistical and computational trade off
Luo Luo, optimization, numerical linear algebra
Zongming Ma, statistics, optimality, social network
Shujie Ma, deep learning, causal inference, network analysis, nonparametric methods, dimensionality reduction, time series data
Eric Ma, network science, graph theory, applied deep learning, applied bayesian statistics
Yi-An Ma, Bayesian inference, time series analysis
Tengyu Ma, deep learning theory
Siyuan Ma, optimization, kernel methods, deep learning
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, decision tree ensembles, compressing large neural networks
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, quantification, text classification, domain adaptation, word embeddings, kernel methods, transfer learning
Dmitriy Morozov, topological data analysis
Christopher Morris, Learning on graphs
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
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, geometric deep learning, topological deep learning
Fabrizio Riguzzi, relational learning, statistical relational learning, inductive logic programming, probabilistic inductive logic programming
Omar Rivasplata, Statistical Learning Theory, Machine Learning / AI Theory, Risk bounds, 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, Differential privacy, deep learning theory, LLMs, high-dimensional statistics, 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
Kean Ming Tan, graphical models, unsupervised learning, low rank approximation
Vincent Tan,
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
Y. Samuel Wang, Graphical Models, Causal Discovery
Mengdi Wang, reinforcement learning, representation learning
Jialei Wang, optimization, high-dimensional statistics, learning theory
Zi Wang, robot learning, Bayesian optimization, learning and planning, Gaussian process, active learning
Yuhao Wang, causal inference, high-dimensional statistics, semiparametric inference, graphical models
Yu-Xiang Wang, statistical machine learning, optimization, differential privacy, reinforcement learning
Lan Wang, high-dimensional statistics, optimal decision estimation, nonparametric and semiparametric statistics, quantile regression, causal inference
Shulei Wang, nonparametric, high-dimensional statistics, machine learning, biomedical application
Serena Wang, fairness, constrained optimization, robust optimization, ensemble methods
Weiran Wang, representation learning, deep learning, speech processing, sequence learning
Chien-Chih Wang, optimization, deep learning, large-scale classification
Chong Wang, approximate inference, deep learning, uncertainty, generative models
Xiaoqian Wang, explainable AI, fairness in machine learning, generative model
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
Zhirong Yang, dimensionality reduction, cluster analysis, visualization
Yuhong Yang, bandit problems, forecasting, model selection and assessment, minimax learning theory
Zhuoran Yang, reinforcement learning, statistical machine learning, optimization
Felix X.-F. Ye, Model Reduction, Dynamical system, Data-driven modeling
Han-Jia Ye, representation learning, meta-learning
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
Yaoliang Yu, generative models, optimization, robustness
Guo Yu, sparsity; convex optimization; Gaussian graphical models; multiple testing
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,
Xinhua Zhang, kernel methods, transfer learning, adversarial learning, representation learning
Chiyuan Zhang, deep learning
Jingzhao Zhang, optimization
Lijun Zhang, Online learning, Bandits, stochastic optimization, Randomized algorithms
Michael Minyi Zhang, Bayesian non-parametrics, MCMC, Gaussian processes
Xin Zhang, Dimension Reduction, Multivariate Analysis and Regression, Tensor Data Analysis, Discriminant Analysis, Neuroimaging
Aonan Zhang, bayesian methods, bayesian nonparametric, deep unsupervised learning, uncertainty estimation
Kun Zhang, causality, transfer learning, kernel methods, unsupervised deep learning
Han Zhao, domain adaptation, domain generalization, transfer learning, probabilistic circuits, algorithmic fairness, multitask learning, meta-learning
Zhigen Zhao, high dimensional statistical inference, empirical Bayesian/Bayesian statistics, Sufficient dimension reduction, multiple comparison
Yunpeng Zhao, Network analysis; Community detection
Peng Zhao, online learning
Tuo Zhao, deep learning, nonconvex optimization, high dimensional statistics, natural language processing, open-source software library
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
Shuchang Zhou, optimization,neural network,quantization
Zhengyuan Zhou, contexutal bandits, online learning, game theory
Ding-Xuan Zhou, deep learning, approximation by deep neural networks, kernel methods, wavelets
Ruoqing Zhu, reinforcement learning, 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
Bernhard Schölkopf, Max Planck Institute for Intelligent Systems, Germany
Terrence Sejnowski, Salk Institute for Biological Studies, USA
Richard Sutton, University of Alberta, Canada
Leslie Valiant, Harvard University, USA