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Journal of Machine Learning Research (JMLR)

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Data-centric Machine Learning Research (DMLR)

TMLR Expert Reviewers

• Alexander Alemi, Google. Variational information bottleneck, deep learning, information theory. [OpenReview] [Google Scholar]
• Yu Bai, Salesforce Research. Deep learning theory, uncertainty quantification, game theory, machine learning theory, reinforcement learning. [OpenReview] [Google Scholar]
• Ahmad Beirami, Google Research. Federated learning, natural language processing, conversational ai, responsible ai, game ai, reinforcement learning, machine learning, information theory, statistics, signal processing. [OpenReview] [Google Scholar]
• Yoshua Bengio, University of Montreal. Deep learning. [OpenReview] [Google Scholar]
• Alberto Bietti, Flatiron Institute. Optimization, kernel methods, deep learning theory. [OpenReview] [Google Scholar]
• Pablo Castro, Google. Reinforcement learning. [OpenReview] [Google Scholar]
• Zachary Charles, Google. Federated learning distributed learning communication-efficient learning. [OpenReview] [Google Scholar]
• Sinho Chewi, Massachusetts Institute of Technology. Optimal transportation, sampling, high-dimensional statistics. [OpenReview] [Google Scholar]
• Kristy Choi, Computer Science Department, Stanford University. Probabilistic models deep generative models unsupervised learning deep learning bayesian inference. [OpenReview] [Google Scholar]
• Sanghyuk Chun, NAVER AI Lab. Multi-modal learning vision and language, machine learning reliability robustness de-biasing domain generalization uncertainty estimation explainability, generative models, audio modelling music modelling, recommender systems matrix factorization collaborative filtering content-based recommender systems, multi-armed bandit online ranking algorithm, dimensionality reduction robust pca clustering. [OpenReview] [Google Scholar]
• Taco Cohen, Qualcomm Inc, QualComm. Reinforcement learning, large language models code generation, generative models deep generative models data compression, group theory representation theory equivariant networks, deep learning representation learning unsupervised learning, causality causal representation learning. [OpenReview] [Google Scholar]
• Ashok Cutkosky, Boston University. Online learning stochastic optimization. [OpenReview] [Google Scholar]
• Valentin De bortoli, University of Oxford. Stochastic optimization, texture synthesis, information geometry, generative modeling, diffusion model, score-matching, schrodinger bridges. [OpenReview]
• Greg Durrett, University of Texas, Austin. Question answering automated reasoning natural language inference, interpretability in nlp, text generation, text summarization, large language models pre-training. [OpenReview] [Google Scholar]
• Vincent Fortuin, Helmholtz AI. Bayesian deep learning, gaussian processes, deep learning, machine learning. [OpenReview] [Google Scholar]
• Tim Genewein, DeepMind. Analysis and understanding of agents, ai safety, bayesian deep learning, variational inference, information theory, rate-distortion theory, network compression, bounded rationality, computational neuroscience, sensorimotor learning. [OpenReview] [Google Scholar]
• Steven Hansen, DeepMind. Intrinsic motivation, unsupervised reinforcement learning, deep reinforcement learning, meta reinforcement learning, model-based reinforcement learning. [OpenReview] [Google Scholar]
• Manuel Haussmann, Aalto University. Bayesian statistics, bayesian deep learning, probabilistic machine learning. [OpenReview] [Google Scholar]
• Matthew Holland, Osaka University. [OpenReview]
• Sanghyun Hong, Oregon State University. Computer security and privacy, machine learning. [OpenReview] [Google Scholar]
• Neil Houlsby, Google. Computer vision, natural language processing, machine learning. [OpenReview] [Google Scholar]
• Maximilian Igl, University of Oxford. Representation learning reinforcement learning, generalization reinforcement learning, hierarchical reinforcement learning, partial observable markov decision process, reinforcement learning planning learning to plan . [OpenReview] [Google Scholar]
• Andrew Ilyas, Massachusetts Institute of Technology. Adversarial examples, robustness, distribution shift. [OpenReview] [Google Scholar]
• Masha Itkina, Toyota Research Institute. Robotics perception sensor fusion, deep learning computer vision convlstms video prediction recurrent models, epistemic uncertainty estimation evidential theory out-of-distribution detection, human behavior modeling occlusion inference mapping, deep generative models variational autoencoders discrete latent spaces. [OpenReview] [Google Scholar]
• Joonas Jälkö, University of Helsinki. Differential privacy bayesian inference variational inference. [OpenReview]
• Fredrik Johansson, Chalmers University of Technology. Counterfactual estimation, causal inference, machine learning for healthcare, machine learning on graphs graph kernels. [OpenReview] [Google Scholar]
• Daniel Johnson, University of Toronto. Uncertainty quantification, decision theory, variational methods, transformers, implicit differentiation, program synthesis, programming languages, continuous relaxation, bayesian inference, probability theory, graph neural networks, structured data, deep learning, neural networks, rnns. [OpenReview] [Google Scholar]
• Yannis Kalantidis, Naver Labs Europe. Self-supervised learning, long-tailed recognition resource-constrained deep learning video understanding, deep learning unsupervised learning representation learning vision and language , clustering nearest neighbor search image retrieval geometry indexing hashing. [OpenReview] [Google Scholar]
• Khimya Khetarpal, McGill University. Reinforcement learning, temporal abstraction, visual attention, hierarchical reinforcement learning. [OpenReview] [Google Scholar]
• Andreas Kirsch, University of Oxford. Uncertainty quantification, active learning, information theory. [OpenReview] [Google Scholar]
• Adhiguna Kuncoro, DeepMind. Deep learning, natural language processing, structured prediction. [OpenReview] [Google Scholar]
• Yunwen Lei, University of Hong Kong. Stochastic optimization, online learning, learning theory. [OpenReview] [Google Scholar]
• Mufan Li, Princeton University. Deep learning theory, learning theory, probability theory, pdes, stochastic analysis, numerical analysis. [OpenReview] [Google Scholar]
• Xi Lin, City University of Hong Kong. Neural combinatorial optimization, multi-task learning, multi-objective optimization, bayesian optimization. [OpenReview] [Google Scholar]
• Xudong Lin, Columbia University. Multimodal content understanding vision-language video analysis representation learning. [OpenReview] [Google Scholar]
• Gabriel Loaiza-ganem, Layer 6 AI. Probabilistic machine learning, deep generative models, bayesian methods, variational inference. [OpenReview] [Google Scholar]
• Wesley Maddox, New York University. Deep learning, probabilistic models, gaussian processes, bayesian deep learning. [OpenReview]
• R. Mccoy, Princeton University. Computational syntax, computational linguistics, inductive bias. [OpenReview] [Google Scholar]
• Elliot Meyerson, Cognizant AI Labs. Surrogate-based optimization evolutionary computation novelty search neural networks, multi-task learning multitask learning meta-learning few-shot learning transfer learning deep learning, graph theory combinatorial optimization. [OpenReview] [Google Scholar]
• Konstantin Mishchenko, Samsung. Stochastic methods optimization, optimization adaptive methods, optimization online learning, algorithms nonconvex optimization smooth optimization, federated learning distributed optimization. [OpenReview] [Google Scholar]
• Jesse Mu, Stanford University. Emergent communication, interpretability, multi-agent communication, language and reinforcement learning, pragmatics, language grounding, deep learning. [OpenReview] [Google Scholar]
• Mirco Mutti, Politecnico di Milano. Reinforcement learning. [OpenReview] [Google Scholar]
• Giannis Nikolentzos, Ecole polytechnique. Graph mining, machine learning on graphs, graph kernels, graph neural networks. [OpenReview] [Google Scholar]
• Atsushi Nitanda, Kyushu Institute of Technology. Deep learning theory, mean-field optimization, kernel method, stochastic optimization. [OpenReview] [Google Scholar]
• Guillermo Ortiz-jimenez, Swiss Federal Institute of Technology Lausanne. Deep learning science, robustness, deep learning, trustworthiness. [OpenReview] [Google Scholar]
• Dylan Paiton, University of Tuebingen. Biological vision, rnns, sparse coding, unsupervised learning, adversarial robustness. [OpenReview] [Google Scholar]
• Jeff Phillips, University of Utah. Computational geometry, coresets and sketches, kernel density estimation, geometric data analysis, spatial scan statistics, high dimensional data. [OpenReview] [Google Scholar]
• Gregory Plumb, Carnegie Mellon University. [OpenReview] [Google Scholar]
• Parikshit Ram, International Business Machines. Hyperdimensional computing, compositional generalization, meta-learning, bilevel optimization, automated machine learning, large scale learning, decision trees, density estimation, clustering, nearest neighbour search. [OpenReview] [Google Scholar]
• Evgenia Rusak, University of Tuebingen. [OpenReview] [Google Scholar]
• Evan Shelhamer, DeepMind. Domain adaptation robustness test-time adaptation, few shot learning infinite mixture modeling nonparametric bayes, locality structure scale-space signal processing, fully convolutional networks, deep learning computer vision recognition. [OpenReview] [Google Scholar]
• Changjian Shui, McGill University. Trustworthy machine learning, medical imaging/healthcare, distribution shift, multitask and transfer learning. [OpenReview]
• Jake Snell, Princeton University. Gaussian processes, generative models, metric learning, few-shot learning. [OpenReview] [Google Scholar]
• Jeremias Sulam, Johns Hopkins University. Interpretability, adversarial robustness, representation learning, sparse representations, dictionary learning. [OpenReview] [Google Scholar]
• Jakub Tomczak, Eindhoven University of Technology. Variational inference, deep generative modeling, deep learning, derivative-free optimization, boltzmann machines, ensemble learning, svm, concept drift, change detection. [OpenReview] [Google Scholar]
• Eleni Triantafillou, Google. Few-shot learning transfer learning self-supervised learning domain generalization domain adaptation, few-shot learning meta-learning, natural language processing sentence representation learning. [OpenReview] [Google Scholar]
• Austin Tripp, University of Cambridge. Generative models, probabilistic models, gaussian process, optimization molecules, generative models molecules chemistry. [OpenReview] [Google Scholar]
• Russell Tsuchida, CSIRO. Point processess, implicit neural networks, deep learning, kernel methods, gaussian processes, probabilistic machine learning. [OpenReview]
• Andrew Wagenmaker, University of Washington, Seattle. Reinforcement learning, bandits, decision and control. [OpenReview]
• Yu-xiong Wang, School of Computer Science, Carnegie Mellon University. Few-shot learning meta-learning transfer learning, human motion prediction. [OpenReview] [Google Scholar]
• Lechao Xiao, Google DeepMind. Mathematics of deep learning/ machine learning, deep learning theory, mathematics. [OpenReview] [Google Scholar]
• Tianyi Zhang, Stanford University. Self-supervised learning, machine learning, natural language processing, graph neural network, language model. [OpenReview] [Google Scholar]
• Tijana Zrnic, Stanford University. Statistical learning theory optimization generalization, selective inference adaptive data analysis fdr control, differential privacy. [OpenReview]

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