# TMLR Editorial Board

### Editors-in-Chief

- Kyunghyun Cho, Genentech and New York University.
- Gautam Kamath, University of Waterloo.
- Hugo Larochelle, Mila and Google DeepMind.
- Naila Murray, Meta AI.

### Past Editors-in-Chief

- Raia Hadsell, Google DeepMind.

### Managing Editors

- Paul Vicol, Google DeepMind.

### Past Managing Editors

- Fabian Pedregosa, Google DeepMind.

### TMLR Action Editors

- Naman Agarwal, Google. Optimization, online learning, optimal control and planning, reinforcement learning theory, differential privacy and optimization. [OpenReview] [Google Scholar]
- Alexander A Alemi, Google. Variational information bottleneck, deep learning, information theory. [OpenReview] [Google Scholar]
- Pierre Alquier, ESSEC Asia-Pacific. Robust estimation, kernel methods, online learning, bayesian nonparametrics, variational inference, approximate bayesian inference, mcmc, pac-bayesian bounds, high-dimensional statistics, aggregation of estimators, statistical learning theory. [OpenReview] [Google Scholar]
- Mauricio A. Álvarez, University of Manchester. Gaussian processes non-parametric bayes dynamical systems kernel methods. [OpenReview] [Google Scholar]
- Bryon Aragam, University of Chicago. Latent variable models, generative models, causal inference, graphical models, nonparametric statistics, statistical learning theory, high-dimensional statistics. [OpenReview] [Google Scholar]
- Cédric Archambeau, Helsing. Neural architecture search, responsible ai, transfer meta- and continual learning, hyperparameter optimization, bayesian optimization, bayesian nonparametrics, gaussian processes, approximate inference, probabilistic machine learning. [OpenReview] [Google Scholar]
- Raman Arora, Johns Hopkins University. Robust adversarial learning, differential privacy, deep multiview learning, canonical correlation analysis, matrix factorization, stochastic optimization, representation learning, online learning. [OpenReview] [Google Scholar]
- Yu Bai, Salesforce Research. Deep learning theory, uncertainty quantification, game theory, machine learning theory, reinforcement learning, large language models. [OpenReview] [Google Scholar]
- Jean Barbier, Abdus Salam international centre for theoretical physics. Graphical models, bayesian inference, spin glasses, machine learning, high-dimensional statistics, random matrix theory, information theory, statistical physics, signal processing. [OpenReview] [Google Scholar]
- Stephen Becker, University of Colorado, Boulder. Convex optimization, sparse recovery. [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]
- Aurélien Bellet, INRIA. Privacy preserving machine learning differential privacy, federated learning decentralized learning distributed learning, fairness in machine learning. [OpenReview] [Google Scholar]
- Srinadh Bhojanapalli, Google. Generalization, optimization, matrix factorization, deep learning. [OpenReview] [Google Scholar]
- Alberto Bietti, Flatiron Institute. Optimization, kernel methods, deep learning theory. [OpenReview] [Google Scholar]
- Yonatan Bisk, Meta. Talking to robots, embodied ai. [OpenReview] [Google Scholar]
- Matthew B. Blaschko, KU Leuven. Deep learning, computer vision, kernel methods. [OpenReview] [Google Scholar]
- Michael Bowling, Department of Computing Science, University of Alberta. Multiagent learning, game theory, reinforcement learning. [OpenReview] [Google Scholar]
- Trevor Campbell, University of British Columbia. Statistical machine learning, probability, bayesian statistics, large-scale data. [OpenReview]
- Yair Carmon, Tel Aviv University. Optimization, machine learning. [OpenReview] [Google Scholar]
- Joao Carreira, Google DeepMind. Computer vision machine learning. [OpenReview] [Google Scholar]
- Pablo Samuel Castro, Google. Reinforcement learning. [OpenReview] [Google Scholar]
- Antoni B. Chan, City University of Hong Kong. Computer vision, deep learning, probabilistic models, time series models. [OpenReview] [Google Scholar]
- Shiyu Chang, UC Santa Barbara. Diffusion model, large language model, fairness, adversarial machine learning, interpretability. [OpenReview] [Google Scholar]
- Zachary Charles, Google. Federated learning distributed learning communication-efficient learning. [OpenReview] [Google Scholar]
- Laurent Charlin, HEC Montreal. Continual learning, combinatorial optimization, dialogue systems, recommender systems, graphical models topic models variational inference. [OpenReview] [Google Scholar]
- Kamalika Chaudhuri, UC San Diego, University of California, San Diego. Theory of robustness, active learning, privacy. [OpenReview] [Google Scholar]
- Swarat Chaudhuri, University of Texas at Austin. Statistical relational learning, neurosymbolic programming, ai safety, reinforcement learning, planning, probabilistic programming, program induction, automated reasoning, formal methods. [OpenReview] [Google Scholar]
- Changyou Chen, State University of New York, Buffalo. Multi-modal learning, vision and language, foundation models, meta learning, deep reinforcement learning, deep generative models, large-scalable bayesian learning, deep learning, bayesian nonparametrics. [OpenReview] [Google Scholar]
- Pin-Yu Chen, International Business Machines. Adversarial machine learning, graph analysis graph mining network science, trustworthy machine learning, adversarial robustness, machine learning and security. [OpenReview] [Google Scholar]
- Seungjin Choi, Intellicode. Bayesian learning, meta-learning, bayesian optimization. [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]
- Nadav Cohen, School of Computer Science, Tel Aviv University. Statistical learning theory deep learning non-convex optimization tensor analysis. [OpenReview] [Google Scholar]
- Ekin Dogus Cubuk, Google. Data augmentation, adversarial examples, robustness of deep learning models, physics simulations, scientific applications of machine learning, molecular dynamics. [OpenReview] [Google Scholar]
- Bo Dai, Georgia Institute of Technology. Reinforcement learning, probabilistic method, kernel method. [OpenReview] [Google Scholar]
- Valentin De Bortoli, University of Oxford. Stochastic optimization, texture synthesis, information geometry, generative modeling, diffusion model, score-matching, schrodinger bridges. [OpenReview]
- Laurent Dinh, Apple. Deep learning, unsupervised learning, generative models, deep invertible models, flow based models, probabilistic inference. [OpenReview] [Google Scholar]
- Vincent Dumoulin, Google. Deep learning computer vision, few-shot learning multi-task learning meta-learning. [OpenReview] [Google Scholar]
- 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]
- Gintare Karolina Dziugaite, McGill University. Data compression, interpretable ml, neural network pruning compression, generalization theory, pac-bayes, deep learning, statistical learning theory, generative models generative adversarial nets, adversarial examples. [OpenReview] [Google Scholar]
- Eric Eaton, University of Pennsylvania. Lifelong learning continual learning, transfer learning multi-task learning, interactive ai interactive ml interpretable ml, perception robotics robot learning robotic control high-level intelligence, precision medicine clinical decision support. [OpenReview] [Google Scholar]
- Murat A Erdogdu, University of Toronto. Optimization, sampling. [OpenReview] [Google Scholar]
- Dumitru Erhan, Google. Deep learning neural networks computer vision object detection. [OpenReview] [Google Scholar]
- Amir-massoud Farahmand, Department of Computer Science, University of Toronto. Reinforcement learning statistical learning theory nonparametric estimators. [OpenReview] [Google Scholar]
- Aleksandra Faust, Google Brain. Natural language processing, meta-learning, task learning, autonomous driving, navigation, reinforcement learning, motion planning, robotics. [OpenReview] [Google Scholar]
- Patrick Flaherty, University of Massachusetts at Amherst. Nonconvex optimization, bayesian statistics, decision-making, hypothesis testing, bioinformatics, computational biology, genetics, variational inference, clustering, mixture models. [OpenReview] [Google Scholar]
- Rémi Flamary, École Polytechnique. Optimal transport, graph data processing, domain adaptation, non convex regularization. [OpenReview] [Google Scholar]
- Vincent Fortuin, Technical University of Munich. Bayesian deep learning, gaussian processes, deep learning, machine learning. [OpenReview] [Google Scholar]
- David Fouhey, New York University. Computer vision 3d reconstruction 3d from a single image, computer vision human-object interaction affordances, ai for science. [OpenReview] [Google Scholar]
- Yanwei Fu, Fudan University. Image inpainting, robotic grasping, sparsity in neural network, learning based 3d reconstruction, facial analysis and person understanding, few-shot learning , zero-shot learning and attribute learning. [OpenReview] [Google Scholar]
- Li Fuxin, Oregon State University. Deep learning, semantic segmentation, video segmentation, explainable deep learning, uncertainty estimation, multi-target tracking, point cloud networks, instance segmentation, bayesian deep learning. [OpenReview] [Google Scholar]
- Zhe Gan, Apple. Deep learning, vision and language, deep generative models. [OpenReview] [Google Scholar]
- Roman Garnett, Washington University in St. Louis. Active learning, gaussian processes, bayesian optimization, bayesian quadrature. [OpenReview] [Google Scholar]
- Matthieu Geist, Cohere. Reinforcement 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]
- Krzysztof J. Geras, NYU Grossman School of Medicine. Medical image analysis, neural networks, unsupervised learning, model evaluation, model aggregation. [OpenReview] [Google Scholar]
- Mohammad Ghavamzadeh, Amazon. Rlhf, bandit algorithms, online learning, reinforcement learning. [OpenReview] [Google Scholar]
- Surbhi Goel, University of Pennsylvania, University of Pennsylvania. Theory, machine learning. [OpenReview] [Google Scholar]
- Mingming Gong, University of Melbourne. Deep generative models, 3d vision, causal discovery & inference. [OpenReview] [Google Scholar]
- Robert M. Gower, Flatiron Institute. Adaptive gradient methods; policy gradient methods;, stochastic optimization; variance reduced methods; stochastic gradient descent; quasi-newton methods; second order methods, numerical linear algebra; sketching. [OpenReview] [Google Scholar]
- Erin Grant, University College London. Computational neuroscience theoretical neuroscience, cognitive science higher-level cognition psychology, deep learning connectionism. [OpenReview] [Google Scholar]
- Edward Grefenstette, Google DeepMind. Machine learning natural language processing neural networks deep learning. [OpenReview] [Google Scholar]
- Shixiang Shane Gu, Google DeepMind. Deep learning reinforcement learning approximate inference probabilistic inference neural networks robotics. [OpenReview] [Google Scholar]
- Benjamin Guedj, University College London, University of London. Learning on graphs, mathematics of deep learning, representation learning, information theory, nonnegative matrix factorization, online learning, pac-bayesian theory, machine learning, statistical learning theory, concentration inequalities, bayesian and quasi-bayesian methods, aggregation theory and ensemble methods, generalisation bounds, sampling algorithms (mcmc ...). [OpenReview] [Google Scholar]
- Caglar Gulcehre, Deepmind. Multiagent deep reinforcement learning, reinforcement learning imitation learning demonstrations attention models, deep learning, nlp natural language understanding, optimization, cognitive science cognitive neuroscience. [OpenReview] [Google Scholar]
- Michael U. Gutmann, University of Edinburgh. Learning energy-based models, experimental design, density ratio estimation, noise-contrastive estimation, likelihood-free inference approximate bayesian computation simulation-based inference. [OpenReview] [Google Scholar]
- Patrick haffner, Interactions Corp.. Natural language processing deep learning. [OpenReview] [Google Scholar]
- Bo Han, HKBU. Out-of-distribution learning, graph representation learning, federated learning, meta and few-shot learning, adversarial learning, label-noise learning, deep learning, weakly supervised 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]
- Ran He, Institute of automation, Chinese academy of science, Chinese Academy of Sciences. Generative model, image generation, face recognition, transfer learning, domain adaptation, image superresolution, representation learning. [OpenReview] [Google Scholar]
- Matthew J. Holland, Osaka University. [OpenReview]
- Sanghyun Hong, Oregon State University. Computer security and privacy, machine learning. [OpenReview] [Google Scholar]
- Antti Honkela, University of Helsinki. Differential privacy, privacy, bayesian machine learning, bioinformatics, gaussian processes. [OpenReview] [Google Scholar]
- Neil Houlsby, Google. Computer vision, natural language processing, machine learning. [OpenReview] [Google Scholar]
- Cho-Jui Hsieh, Google. Deep learning, optimization. [OpenReview] [Google Scholar]
- Furong Huang, Department of Computer Science, University of Maryland. Generative ai, reinforcement learning, deep learning theory, spectral methods tensor decomposition, mixture models latent variable models, social network analysis. [OpenReview] [Google Scholar]
- Jia-Bin Huang, University of Maryland, College Park. Machine learning, computer vision. [OpenReview] [Google Scholar]
- W Ronny Huang, Google. Speech, large language models. [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]
- Stanislaw Kamil Jastrzebski, Molecule.one. Deep learning deep learning theory optimization loss surface transfer learning, cheminformatics drug discovery reaction outcome prediction molecule property prediction. [OpenReview] [Google Scholar]
- Dinesh Jayaraman, School of Engineering and Applied Science, University of Pennsylvania. Reinforcement learning, robotics, robot learning, embodied ai, unsupervised feature learning, visual recognition. [OpenReview] [Google Scholar]
- Kui Jia, South China University of Technology. Learning and generalization, explainable ai; interpretable ai, deep learning surface reconstruction, 3d detection; object pose estimation, manipulation and grasping, deep transfer learning. [OpenReview] [Google Scholar]
- Fredrik D. Johansson, Chalmers University of Technology. Counterfactual estimation, causal inference, machine learning for healthcare, machine learning on graphs graph kernels. [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]
- Varun Kanade, University of Oxford. Optimization, deep learning, randomized algorithms, random graphs, computational learning theory. [OpenReview]
- Sungwoong Kim, Korea University. Artificial general intelligence deep learning meta learning representation learning generative modeling reinforcement learning optimization multi-modal learning language modeling, graphical modeling structured support vector machine speech recognition image segmentation. [OpenReview] [Google Scholar]
- Brian Kingsbury, IBM. Speech recognition spoken language understanding deep learning. [OpenReview] [Google Scholar]
- Simon Kornblith, Anthropic. Deep learning, representation learning, transfer learning, analysis of neural network representations, neuroscience. [OpenReview] [Google Scholar]
- Florent Krzakala, Swiss Federal Institute of Technology Lausanne. Statistical physics, high-dimensional asymptotics, universality. [OpenReview] [Google Scholar]
- Alp Kucukelbir, Columbia University. Bayesian inference approximate inference variational inference, probabilistic programming. [OpenReview] [Google Scholar]
- Brian Kulis, Boston University. Few-shot learning domain adaptation meta learning, metric learning hashing similarity search, clustering bregman divergences image segmentation, bayesian nonparametrics dirichlet processes graphical models. [OpenReview] [Google Scholar]
- Abhishek Kumar, Google DeepMind. Machine learning deep learning representation learning generative models distribution shift label noise transfer learning. [OpenReview] [Google Scholar]
- Branislav Kveton, Amazon. Bandits, recommender systems, learning to rank, online learning, markov decision processes, reinforcement learning. [OpenReview] [Google Scholar]
- Anastasios Kyrillidis, Rice University. Non-convex optimization, convex optimization, large-scale computing. [OpenReview] [Google Scholar]
- Simon Lacoste-Julien, University of Montreal. Deep learning theory, large scale optimization, convex optimization, structured prediction, topic models. [OpenReview] [Google Scholar]
- Marc Lanctot, Google DeepMind. Computational game theory multiagent learning reinforcement learning planning game tree search. [OpenReview] [Google Scholar]
- Jaehoon Lee, Google. Deep learning, theoretical physics, machine learning. [OpenReview] [Google Scholar]
- Kangwook Lee, University of Wisconsin, Madison. Large language models, diffusion models, deep learning, fairness in machine learning, distributed machine learning, information theory. [OpenReview] [Google Scholar]
- Stefan Lee, Oregon State University. Computer vision machine learning deep learning language and vision. [OpenReview] [Google Scholar]
- Robert Legenstein, Graz University of Technology. Deep learning, spiking neural networks, computational neuroscience. [OpenReview] [Google Scholar]
- Yunwen Lei, University of Hong Kong. Stochastic optimization, learning theory. [OpenReview] [Google Scholar]
- Bo Li, University of Illinois, Urbana Champaign. Trustworthy machine learning. [OpenReview] [Google Scholar]
- Hongsheng Li, The Chinese University of Hong Kong. Unsupervised domain adapation, semi-supervised learning, 3d object detection, object detection, feature distillation, semantic segmentation. [OpenReview] [Google Scholar]
- Lei Li, School of Computer Science, Carnegie Mellon University. Molecule learning and drug discovery, machine translation speech translation, natural language processing text generation large language models, data mining time series analysis, machine learning. bayesian methods deep learning. [OpenReview] [Google Scholar]
- Lihong Li, Amazon. Contextual bandit, reinforcement learning. [OpenReview] [Google Scholar]
- Yang Li, Google. Natural language processing, deep learning, machine learning, human computer interaction. [OpenReview] [Google Scholar]
- Yingzhen Li, Imperial College London. Continue learning, disentangled representation, gaussian process, meta learning, stochastic gradient mcmc, adversarial attacks and defences, score matching, stein's method, bayesian neural networks, deep generative models, transfer learning, approximate inference, variational inference, message passing. [OpenReview] [Google Scholar]
- Yixuan Li, University of Wisconsin, Madison. Deep learning, out-of-distribution detection, ai safety. [OpenReview] [Google Scholar]
- Yujia Li, Google DeepMind. Language modeling, program synthesis, deep learning. [OpenReview] [Google Scholar]
- Yingbin Liang, The Ohio State University. Machine learning, reinforcement learning, large-scale optimization. [OpenReview]
- Hsuan-Tien Lin, National Taiwan University. Complementary-label learning, active learning, multi-label learning, cost-sensitive classification. [OpenReview] [Google Scholar]
- Xi Lin, City University of Hong Kong. Neural combinatorial optimization, multi-task learning, multi-objective optimization, bayesian optimization. [OpenReview] [Google Scholar]
- Tongliang Liu, University of Sydney. Learning with noisy labels, weakly supervised learning, adversarial learning, transfer learning. [OpenReview] [Google Scholar]
- Wei Liu, Tencent. Deep learning, information retrieval, big data, machine learning, computer vision, pattern recognition. [OpenReview] [Google Scholar]
- Yan Liu, University of Southern California. Time series and spatiotemporal data analysis, social network analysis topic models. [OpenReview] [Google Scholar]
- Gabriel Loaiza-Ganem, Layer 6 AI. Probabilistic machine learning, deep generative models, bayesian methods, variational inference, manifold learning. [OpenReview] [Google Scholar]
- Mingsheng Long, Tsinghua University. World model, time series analysis, neural operators, reinforcement learning, physics-informed neural network, scientific machine learning, transformers, domain generalization, out-of-distribution generalization, few-shot learning, foundation model, predictive learning, spatiotemporal learning, sequence model, multitask learning, multimodal learning, deep learning, transfer learning, domain adaptation. [OpenReview] [Google Scholar]
- Kuldeep S. Meel, Department of Computer Science. Reasoning under uncertainty, sat solving. [OpenReview]
- Nishant A Mehta, University of Victoria. Multi-armed bandits, pac-bayes, online learning, prediction with expert advice, online convex optimization, sparse coding, lasso, multi-task learning, transfer learning, lifelong learning, statistical learning theory. [OpenReview] [Google Scholar]
- Aditya Krishna Menon, Google. Proper losses surrogate losses classification calibration, long-tail learning class imbalance, distillation label smoothing. [OpenReview]
- 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]
- Olgica Milenkovic, University of Illinois, Urbana Champaign. Machine learning, coding theory, bioinformatics, molecular storage and computing. [OpenReview] [Google Scholar]
- Andrew Miller, Apple. Probabilistic modeling, variational inference, generative modeling, health, medicine, approximate inference. [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]
- Bamdev Mishra, Microsoft. Min-max optimization, optimal transport theory and applications, cross-lingual embeddings and alignment, manifold optimization. [OpenReview] [Google Scholar]
- Andriy Mnih, DeepMind. Discrete latent variables gradient estimation, variational inference generative models, generative modelling. [OpenReview] [Google Scholar]
- Mirco Mutti, Technion - Israel Institute of Technology, Technion. Reinforcement learning. [OpenReview] [Google Scholar]
- Shinichi Nakajima, TU Berlin. Generative models, monte carlo sampling, variational inference, bayesian learning. [OpenReview] [Google Scholar]
- Karthik R Narasimhan, Princeton University. Reinforcement learning, deep learning, natural language processing. [OpenReview] [Google Scholar]
- Gergely Neu, Universitat Pompeu Fabra. Stochastic optimization convex optimization, online learning bandit problems learning theory, reinforcement learning theory. [OpenReview] [Google Scholar]
- Vlad Niculae, University of Amsterdam. Generative models, latent variable models, structured prediction, convex optimization, argmin differentiation, natural language processing, computational social science. [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]
- Gang Niu, RIKEN. Deep learning and representation learning, weakly supervised learning, semi-supervised learning. [OpenReview] [Google Scholar]
- Alain Oliviero Durmus, École Polytechnique. Variational inference, stochastic approximation, stochastic optimization, markov chain monte carlo, monte carlo methods, stochastic processes. [OpenReview]
- Lorenzo Orecchia, Boston University. Convex optimization, graph algorithms. [OpenReview] [Google Scholar]
- George Papamakarios, DeepMind. Generative models explicit-likelihood models normalizing flows, approximate bayesian inference variational inference simulation-based inference. [OpenReview] [Google Scholar]
- Jaakko Peltonen, Tampere University. Ethical ai, text data analysis, information visualization, exploratory data analysis, machine learning. [OpenReview] [Google Scholar]
- Jeffrey Pennington, Google. Theory of deep learning, geometry of neural networks, recurrent neural networks, word embeddings, theoretical understanding of neural networks, recursive neural networks, sentiment analysis. [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]
- Pascal Poupart, University of Waterloo. Reinforcement learning markov decision processes, federated learning, inverse constraint reinforcement learning. [OpenReview] [Google Scholar]
- Tao Qin, Microsoft Research Asia. Deep learning, neural machine translation, neural speech synthesis, deep reinforcement learning, pre-training, molecular modeling, drug discovery. [OpenReview] [Google Scholar]
- Novi Quadrianto, University of Sussex. Privileged learning, multi-task and transfer learning, ethical machine learning. [OpenReview] [Google Scholar]
- Guillaume Rabusseau, Montreal Institute for Learning Algorithms, University of Montreal, University of Montreal. Tensor networks, weighted automata, tensor methods. [OpenReview] [Google Scholar]
- Colin Raffel, Hugging Face. Machine learning deep learning, language models large language models, semi-supervised learning, transfer learning, unsupervised learning. [OpenReview] [Google Scholar]
- Tom Rainforth, University of Oxford. Experimental design bayesian optimal experimental design active learning, deep generative models variational autoencoders, approximate inference variational inference, monte carlo methods. [OpenReview] [Google Scholar]
- Marcello Restelli, Politecnico di Milano. Machine learning, reinforcement learning. [OpenReview] [Google Scholar]
- Blake Aaron Richards, McGill University. Deep learning neuroscience credit assignment, reinforcement learning memory neuroscience, neuroinformatics. [OpenReview] [Google Scholar]
- Peter Richtárik, King Abdullah University of Science and Technology (KAUST). Randomized second order methods, federated learning, distributed optimization, randomized algorithms, supervised machine learning, stochastic gradient descent, randomized coordinate descent, optimization. [OpenReview] [Google Scholar]
- Marcus Rohrbach, Technische Universität Darmstadt. Visual question answering, vision and language, computer vision, reliable ai, multimodal ai. [OpenReview] [Google Scholar]
- Daniel M. Roy, University of Toronto. Statistical learning theory, bayesian statistics, online learning, deep learning. [OpenReview] [Google Scholar]
- Francisco J. R. Ruiz, DeepMind. Probabilistic models variational inference, generative models variational autoencoders, topic models, bayesian nonparametrics. [OpenReview] [Google Scholar]
- Sivan Sabato, Ben-Gurion University of the Negev. Theoretical machine leaning, active learning, interactive learning, statistical learning theory. [OpenReview] [Google Scholar]
- Frederic Sala, University of Wisconsin, Madison. Learning with limited supervision, representation learning, semisupervised & weakly supervised learning, non-euclidean & geometric machine learning. [OpenReview] [Google Scholar]
- Mathieu Salzmann, Swiss Federal Institute of Technology Lausanne. Deep learning compact models, domain adaptation computer vision, pose estimation human pose estimation. [OpenReview] [Google Scholar]
- Jonathan Scarlett, National University of Singapore. Information theory information-theoretic limits communication, bayesian optimization gaussian processes bandit algorithms, sparsity group testing compressive sensing generative priors. [OpenReview] [Google Scholar]
- Jessica Schrouff, Google DeepMind. Deep learning fairness, health time series deep learning rnns, deep learning explainability, health model interpretation explainability. [OpenReview] [Google Scholar]
- Ozan Sener, Apple. Derivative free optimization, random search, deep learning, multi task learning, active learning, conditional random fields, structured prediction. [OpenReview] [Google Scholar]
- Nihar B Shah, Carnegie Mellon University. Crowdsourcing, ranking, peer review. [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]
- Jinwoo Shin, Korea Advanced Institute of Science and Technology. Self/semi-supervised learning, deep reinforcement learning, generative adversarial networks, out-of-distribution detection and generalization. [OpenReview] [Google Scholar]
- Florian Shkurti, Department of Computer Science, University of Toronto. Robotics control reinforcement learning imitation learning 3d computer vision perception variational inference generative models. [OpenReview]
- Changjian Shui, Vector Institute. Trustworthy machine learning, medical imaging/healthcare, machine learning under distribution shift, multitask and transfer learning. [OpenReview] [Google Scholar]
- Vikas Sindhwani, Google. Manifold learning, numerical optimization, robotics control reinforcement learning, semi-supervised learning, kernel methods. [OpenReview] [Google Scholar]
- Virginia Smith, Carnegie Mellon University. [OpenReview]
- Jake Snell, Princeton University. Gaussian processes, generative models, metric learning, few-shot learning. [OpenReview] [Google Scholar]
- Jasper Snoek, Google. Machine learning, bayesian optimization, deep learning, bayesian deep learning, gaussian processes, uncertainty and robustness for deep learning. [OpenReview] [Google Scholar]
- Yale Song, Facebook AI Research. Representation learning, multimodal learning, computer vision. [OpenReview] [Google Scholar]
- Alessandro Sordoni, Microsoft. Robustness, ml for nlp, deep learning, unsupervised learning. [OpenReview]
- Alessandro Sperduti, Universita' degli studi di Padova. Process mining, neural networks rnns deep learning. [OpenReview] [Google Scholar]
- Pablo Sprechmann, DeepMind. Representation learning deep learning sparse modeling audio processing computer vision . [OpenReview] [Google Scholar]
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- Jeremias Sulam, Johns Hopkins University. Interpretability, adversarial robustness, representation learning, sparse representations, dictionary learning. [OpenReview] [Google Scholar]
- Ruoyu Sun, University of Illinois, Urbana-Champaign. Convex optimization, nonconvex optimization, deep learning, generative adversarial networks. [OpenReview] [Google Scholar]
- Kevin Swersky, Google Brain. Machine learning. [OpenReview] [Google Scholar]
- Xu Tan, Microsoft. Language speech and audio, text to speech, machine translation, speech recognition, ai music, talking face synthesis. [OpenReview] [Google Scholar]
- Daniel Tarlow, Mila Quebec AI Institute. Graph neural networks, program synthesis, source code, machine learning for code, machine learning for software engineering. [OpenReview] [Google Scholar]
- Bertrand Thirion, INRIA. Neuroscience brain imaging cognition, statistical inference high-dimensional data, ai systems brain organization. [OpenReview] [Google Scholar]
- Nicolas THOME, Université Pierre et Marie Curie - Paris 6, Sorbonne Université - Faculté des Sciences (Paris VI). Machine learning ; deep learning, computer vision; medical applications ; time series, physics informed machine learning, robustness. [OpenReview] [Google Scholar]
- Jakub M. 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]
- Sebastian Tschiatschek, University of Vienna. [OpenReview]
- Russell Tsuchida, CSIRO. Point processess, implicit neural networks, deep learning, kernel methods, gaussian processes, probabilistic machine learning. [OpenReview]
- Jonathan Ullman, Northeastern University. Differential privacy, ml theory. [OpenReview] [Google Scholar]
- Antonio Vergari, University of Edinburgh. Neuro-symbolic reasoning, probabilistic circuits, anomaly detection, deep learning, representation learning, multi-label classification, probabilistic learning, machine learning probabilistic graphical models representation learning, machine learning, probabilistic graphical models, tractable probabilistic models, exact inference. [OpenReview] [Google Scholar]
- Ellen Vitercik, Stanford University. Mechanism design, sample complexity, learning theory. [OpenReview] [Google Scholar]
- Matthew R. Walter, Toyota Technological Institute at Chicago. Deep learning, reinforcement learning, natural language grounding, natural language generation, robot manipulation, human-robot interaction, robot learning, robotics, computer vision, slam. [OpenReview] [Google Scholar]
- Naigang Wang, IBM, International Business Machines. Deep learning quantization low precision sparsity pruning accelerator compression. [OpenReview] [Google Scholar]
- Yunhe Wang, Huawei Noah's Ark Lab. Deep neural networks, machine learning, computer vision. [OpenReview] [Google Scholar]
- Ying Wei, Nanyang Technological University. Transfer learning domain adaptation meta-learning multitask learning, automatic machine learning, sentiment classification. [OpenReview] [Google Scholar]
- Adam White, University of Alberta. Deeplearning, robotics, reinforcement learning. [OpenReview] [Google Scholar]
- Martha White, University of Alberta. Reinforcement learning, representation learning, time series. [OpenReview] [Google Scholar]
- Sinead A Williamson, Apple. Network models social networks graphs, bayesian nonparametrics, bayesian inference mcmc probabilistic modeling bayesian statistics. [OpenReview] [Google Scholar]
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