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Journal of Machine Learning Research (JMLR)

Proceedings of Machine Learning Research (PMLR)

Data-centric Machine Learning Research (DMLR)

TMLR Expert Reviewers

2024

• Sai Aparna Aketi, Meta. Privacy preserving machine learning, federated learning, decentralized optimization, radiation hardening by design techniques. [OpenReview] [Google Scholar]
• Han Bao, Kyoto University, Kyoto University. Learning theory, adversarial robustness, representation learning, self-supervised learning, loss function. [OpenReview] [Google Scholar]
• Emmanuel Bengio, Recursion. Machine learning deep learning reinforcement learning. [OpenReview] [Google Scholar]
• Michael Boratko, Google. Core machine learning deep learning optimization graph modeling. [OpenReview] [Google Scholar]
• Neil Burch, Sony AI. Multiagent learning, game theory, search. [OpenReview] [Google Scholar]
• Mark J. Coates, McGill University. Recommender systems, graph learning, bayesian inference, particle filters. [OpenReview] [Google Scholar]
• Ian Connick Covert, Stanford University. Multi-modality, amortization, interpretability, feature selection. [OpenReview] [Google Scholar]
• Gergely Flamich, University of Cambridge. Algorithmic information theory; kolmogorov complexity, point processes poisson processes, compression image compression learned compression, entropy coding relative entropy coding channel simulation reverse channel simulation. [OpenReview] [Google Scholar]
• Maxime Gasse, ServiceNow. Causality, reinforcement learning, combinatorial optimization, structure learning, probabilistic graphical models directed acyclic graphs. [OpenReview] [Google Scholar]
• Efstratios Gavves, University of Amsterdam. Dynamical deep learning causal representation learning physics-informed deep learning, video understanding deep learning, fine-grained classification computer vision geometry in vision. [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, University of Southern Denmark - SDU. Bayesian statistics, bayesian deep learning, probabilistic machine learning, reinforcement learning. [OpenReview] [Google Scholar]
• Xuming He, ShanghaiTech University. Few-shot learning incremental learning, image caption generation visual relation detection, graph neural network, semantic segmentation scene parsing object segmentation. [OpenReview] [Google Scholar]
• Markus Heinonen, Aalto University. Gaussian process, deep learning, kernel methods, differential equations. [OpenReview] [Google Scholar]
• Matthew J. Holland, Osaka University. Learning theory. [OpenReview] [Google Scholar]
• Takashi Ishida, RIKEN. Bayes error estimation, overfitting regularization, semi-supervised learning weakly supervised learning. [OpenReview] [Google Scholar]
• Daniel D. 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]
• Taylor W. Killian, Apple. Risk-sensitive reinforcement learning, offline reinforcement learning, causal inference, approximate inference, reinforcement learning, transfer learning, healthcare, bayesian neural networks. [OpenReview] [Google Scholar]
• Andreas Kirsch, GenAI Startup. Uncertainty quantification, active learning, information theory. [OpenReview] [Google Scholar]
• Arto Klami, University of Helsinki. Prior elicitation, computational physics ultrasound, data integration canonical correlation analysis data fusion, inference markov chain monte carlo variational inference, bayesian machine learning, spectral imaging computer vision, computational neuroscience meg. [OpenReview] [Google Scholar]
• Aaron Klein, Amazon Berlin. Bayesian optimization hyperparameter optimization neural-architecture search automl. [OpenReview] [Google Scholar]
• Adhiguna Kuncoro, DeepMind. Deep learning, natural language processing, structured prediction. [OpenReview] [Google Scholar]
• Antoine Ledent, Singapore Management University. Matrix completion, statistical learning theory, interpretability regularisation, stochastic analysis. [OpenReview] [Google Scholar]
• Jonathan Lee, Stanford University. Reinforcement learning, bandits, online learning, imitation learning. [OpenReview] [Google Scholar]
• Tian Li, University of Chicago. [OpenReview] [Google Scholar]
• Tatiana Likhomanenko, Apple. Deep learning speech recognition nlp image recognition, deep learning face recognition image recognition video, high energy physics gradient boosting optimization model compression loss functions. [OpenReview] [Google Scholar]
• Gabriel Loaiza-Ganem, Layer 6 AI. Probabilistic machine learning, deep generative models, bayesian methods, variational inference, manifold learning. [OpenReview] [Google Scholar]
• Wesley J Maddox, New York University. Deep learning, probabilistic models, gaussian processes, bayesian deep learning. [OpenReview]
• Audra McMillan, Apple. Privacy, differential privacy, statistics. [OpenReview]
• Aaron Mishkin, Computer Science Department, Stanford University. Optimization convex optimization optimization for deep learning. [OpenReview] [Google Scholar]
• Mirco Mutti, Technion - Israel Institute of Technology. Reinforcement learning. [OpenReview] [Google Scholar]
• Samuel Neumann, University of Alberta. Reinforcement learning, policy optimization, actor-critic. [OpenReview]
• Kenta Oono, Preferred Networks, Inc.. Deep learning learning theory cnns graph neural networks. [OpenReview] [Google Scholar]
• Guillermo Ortiz-Jimenez, Google DeepMind. Deep learning science, robustness, deep learning, trustworthiness. [OpenReview] [Google Scholar]
• Matteo Papini, Polytechnic Institute of Milan. Reinforcement learning. [OpenReview] [Google Scholar]
• Oleksandr Shchur, Amazon. Time series forecasting, temporal point processes, generative models. [OpenReview] [Google Scholar]
• Jacopo Teneggi, Johns Hopkins University. Uncertainty quantification, interpretability. [OpenReview] [Google Scholar]
• Austin Tripp, Valence Labs powered by recursion. Generative models, probabilistic models, gaussian process, optimization molecules, generative models molecules chemistry. [OpenReview] [Google Scholar]
• Dennis Wei, International Business Machines. Explainability attribution, fairness, trustworthy, interpretability. [OpenReview] [Google Scholar]
• Tijana Zrnic, Stanford University. [OpenReview]

2023

• Alexander A Alemi, Google Deepmind. Variational information bottleneck, deep learning, information theory. [OpenReview] [Google Scholar]
• Yu Bai, OpenAI. Deep learning theory, uncertainty quantification, game theory, machine learning theory, reinforcement learning, large language models. [OpenReview] [Google Scholar]
• Ahmad Beirami, Google. Federated learning, natural language processing, conversational ai, language models, 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 Samuel Castro, Google. Reinforcement learning. [OpenReview] [Google Scholar]
• Zachary Charles, Google. Federated learning distributed learning communication-efficient learning. [OpenReview] [Google Scholar]
• Sinho Chewi, Yale University. Optimal transportation, sampling, high-dimensional statistics. [OpenReview] [Google Scholar]
• Kristy Choi, Facebook. 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, Technical University of Munich. 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, University of Southern Denmark - SDU. Bayesian statistics, bayesian deep learning, probabilistic machine learning, reinforcement learning. [OpenReview] [Google Scholar]
• Matthew J. Holland, Osaka University. Learning theory. [OpenReview] [Google Scholar]
• 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, NVIDIA. Traffic models autonomous vehicles simulation, 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, Stanford University. 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, robot policy evaluation, failure detection and mitigation, active learning. [OpenReview] [Google Scholar]
• Joonas Jälkö, University of Helsinki. Differential privacy bayesian inference variational inference. [OpenReview]
• Fredrik D. Johansson, Chalmers University of Technology. Counterfactual estimation, causal inference, machine learning for healthcare, machine learning on graphs graph kernels. [OpenReview] [Google Scholar]
• Daniel D. 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 distillation, visual representation 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, Google. Reinforcement learning, temporal abstraction, hierarchical reinforcement learning, continual learning. [OpenReview] [Google Scholar]
• Andreas Kirsch, GenAI Startup. 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. Stability and generalization, stochastic optimization, learning theory. [OpenReview] [Google Scholar]
• Mufan Bill 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, manifold learning. [OpenReview] [Google Scholar]
• Wesley J Maddox, New York University. Deep learning, probabilistic models, gaussian processes, bayesian deep learning. [OpenReview]
• R. Thomas McCoy, Yale University. Computational syntax, computational linguistics, inductive bias, tensor product representations, neural network interpretability. [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, Facebook. [OpenReview] [Google Scholar]
• Jesse Mu, Anthropic. Emergent communication, interpretability, multi-agent communication, language and reinforcement learning, pragmatics, language grounding, deep learning. [OpenReview] [Google Scholar]
• Mirco Mutti, Technion - Israel Institute of Technology. Reinforcement learning. [OpenReview] [Google Scholar]
• Giannis Nikolentzos, University of Peloponnese. Graph mining, machine learning on graphs, graph kernels, graph neural networks. [OpenReview] [Google Scholar]
• Atsushi Nitanda, A*STAR. Deep learning theory, mean-field optimization, kernel method, stochastic optimization. [OpenReview] [Google Scholar]
• Guillermo Ortiz-Jimenez, Google DeepMind. Deep learning science, robustness, deep learning, trustworthiness. [OpenReview] [Google Scholar]
• Dylan M. Paiton, University of Tuebingen. Biological vision, rnns, sparse coding, unsupervised learning, adversarial robustness. [OpenReview] [Google Scholar]
• Jeff M. 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, Google 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, Vector Institute. Reliable machine learning, algorithmic fairness, machine learning under distribution shift. [OpenReview] [Google Scholar]
• 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 M. Tomczak, Eindhoven University of Technology. Variational inference, generative ai, 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, Valence Labs powered by recursion. Generative models, probabilistic models, gaussian process, optimization molecules, generative models molecules chemistry. [OpenReview] [Google Scholar]
• Russell Tsuchida, Monash University. Information geometry, point processess, deep learning, kernel methods, gaussian processes, probabilistic machine learning, implicit neural networks. [OpenReview]
• Andrew Wagenmaker, University of California, Berkeley. Reinforcement learning, bandits, decision and control. [OpenReview] [Google Scholar]
• 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. [OpenReview]

© TMLR 2024.