# TMLR Editorial Board

### Editors-in-Chief

- Kyunghyun Cho, New York University.
- Raia Hadsell, DeepMind.
- Hugo Larochelle, Mila and Google.

### Managing Editors

- Fabian Pedregosa, Google.

### TMLR Action Editors

- Naman Agarwal, Google. Optimization, online learning, optimal control and planning, reinforcement learning theory, differential privacy and optimization. [OpenReview] [Google Scholar]
- Alexander Alemi, Google. Variational information bottleneck, deep learning, information theory. [OpenReview] [Google Scholar]
- Pierre Alquier, RIKEN AIP. 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 Álvarez, University of Manchester. Gaussian processes non-parametric bayes dynamical systems kernel methods. [OpenReview] [Google Scholar]
- Cédric Archambeau, Amazon Web Services. Neural architecture search, fairness and bias mitigation, 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]
- Artem Babenko, Yandex. Generative models in computer vision, deep learning for tabular data. [OpenReview] [Google Scholar]
- Pierre-luc Bacon, University of Montreal. Reinforcement learning temporal abstraction hierarchical reinforcement learning continual learning policy gradient methods off-policy learning. [OpenReview] [Google Scholar]
- David Balduzzi, XTX Markets. Time series, rnns, optimization, game theory, multi-agent, domain adaptation. [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]
- 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]
- Yonatan Bisk, Carnegie Mellon University. Vision and language, language grounding, vision language navigation, alfred. [OpenReview] [Google Scholar]
- Matthew Blaschko, KU Leuven. Deep learning, computer vision, kernel methods. [OpenReview] [Google Scholar]
- Michael Bowling, DeepMind. Multiagent learning, game theory, reinforcement learning. [OpenReview] [Google Scholar]
- Joan Bruna, New York University. Deep neural networks stochastic processes unsupervised learning. [OpenReview] [Google Scholar]
- Thang Bui, Australian National University. Bayesian deep learning, generative models/latent variable models/state space models, bayesian inference and approximate bayesian inference, gaussian processes. [OpenReview] [Google Scholar]
- Yair Carmon, Tel Aviv University. Optimization, machine learning. [OpenReview] [Google Scholar]
- Joao Carreira, DeepMind. Computer vision machine learning. [OpenReview] [Google Scholar]
- Antoni Chan, City University of Hong Kong. Computer vision, deep learning, probabilistic models, time series models. [OpenReview] [Google Scholar]
- Shiyu Chang, UC Santa Barbara. Fairness, adversarial machine learning, interpretability. [OpenReview] [Google Scholar]
- Laurent Charlin, HEC Montreal. Continual learning, combinatorial optimization, dialogue systems, recommender systems, graphical models topic models variational inference. [OpenReview] [Google Scholar]
- Swarat Chaudhuri, University of Texas, 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]
- Alexandra Chouldechova, Amazon. Fairness algorithmic bias bias detection bias mitigation, explainability transparency, human-computer-interaction user studies human-in-the-loop algorithm-in-the-loop. [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 Cubuk, Google. Data augmentation, adversarial examples, robustness of deep learning models, physics simulations, scientific applications of machine learning, molecular dynamics. [OpenReview] [Google Scholar]
- Marco Cuturi, Apple. Optimal transport, optimization, differentiable programming. [OpenReview] [Google Scholar]
- Bo Dai, Google Brain. Reinforcement learning, probabilistic method, kernel method. [OpenReview] [Google Scholar]
- Yann Dauphin, Google. Machine learning, deep learning. [OpenReview] [Google Scholar]
- Debadeepta Dey, Microsoft Research. Neural architecture search, deep learning, reinforcement learning, robotics. [OpenReview] [Google Scholar]
- Laurent Dinh, Apple. Deep learning, unsupervised learning, generative models, deep invertible models, flow based models, probabilistic inference. [OpenReview] [Google Scholar]
- Arnaud Doucet, Google DeepMind. Bayesian statistics denoising diffusion models monte carlo methods markov chain monte carlo methods latent variable models appproximate inference variational methods. [OpenReview] [Google Scholar]
- Vincent Dumoulin, Google. Deep learning computer vision, few-shot learning multi-task learning meta-learning. [OpenReview] [Google Scholar]
- Alain Durmus, Ecole Normale Superieure Paris Saclay. Variational inference, stochastic approximation, stochastic optimization, markov chain monte carlo, monte carlo methods, stochastic processes. [OpenReview]
- Krishnamurthy Dvijotham, Google Brain. Deep learning, optimization, control theory, machine learning. [OpenReview]
- Murat 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, 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]
- David Fouhey, University of Michigan. Computer vision 3d reconstruction 3d from a single image, computer vision human-object interaction affordances. [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]
- Yarin Gal, University of Oxford. Bayesian deep learning. [OpenReview] [Google Scholar]
- Zhe Gan, Microsoft. Deep learning, vision and language, deep generative models. [OpenReview] [Google Scholar]
- Roman Garnett, Washington University, St. Louis. Active learning, gaussian processes, bayesian optimization, bayesian quadrature. [OpenReview] [Google Scholar]
- Matthieu Geist, Google. Reinforcement learning. [OpenReview] [Google Scholar]
- Krzysztof Geras, NYU Grossman School of Medicine. Medical image analysis, neural networks, unsupervised learning, model evaluation, model aggregation. [OpenReview] [Google Scholar]
- Mohammad Ghavamzadeh, Google Research. Bandit algorithms, online learning, reinforcement learning. [OpenReview] [Google Scholar]
- Mingming Gong, University of Melbourne. Causal learning and reasoning, transfer learning (domain adaptation/generalization few-shot learning), computer vision. [OpenReview] [Google Scholar]
- Robert 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]
- Edward Grefenstette, Facebook. Machine learning natural language processing neural networks deep learning. [OpenReview] [Google Scholar]
- Shixiang Gu, Google. Deep learning reinforcement learning approximate inference probabilistic inference neural networks robotics. [OpenReview] [Google Scholar]
- Benjamin Guedj, University College 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 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]
- András György, DeepMind. Online learning and optimization bandits, learning theory, adversarial examples. [OpenReview] [Google Scholar]
- David Ha, Google. Machine learning. [OpenReview] [Google Scholar]
- Patrick Haffner, Interactions Corp.. Natural language processing deep learning. [OpenReview] [Google Scholar]
- Bo Han, HKBU. Causal representation learning, graph representation learning, meta and few-shot learning, adversarial learning, label-noise learning, deep learning, weakly supervised 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, machine learning. [OpenReview] [Google Scholar]
- Jia-bin Huang, University of Maryland, College Park. Machine learning, computer vision. [OpenReview] [Google Scholar]
- W ronny Huang, Google. Speech, language models, deep learning, poisoning. [OpenReview] [Google Scholar]
- Stanislaw 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, 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]
- Jiantao Jiao, University of California Berkeley. Information theory statistics machine learning optimization. [OpenReview] [Google Scholar]
- Danilo Jimenez rezende, DeepMind. Generative models variational inference physics flows probability differential geometry equivariance. [OpenReview] [Google Scholar]
- Gautam Kamath, University of Waterloo. Robustness, data privacy. [OpenReview] [Google Scholar]
- Varun Kanade, University of Oxford. Optimization, deep learning, randomized algorithms, random graphs, computational learning theory. [OpenReview]
- Brian Kingsbury, IBM. Speech recognition spoken language understanding deep learning. [OpenReview] [Google Scholar]
- Zsolt Kira, Georgia Institute of Technology. Deep learning clustering unsupervised learning object discovery, semi-supervised learning self-supervised learning few-shot learning continual learning, object detection scene understanding multi-modal fusion. [OpenReview] [Google Scholar]
- Simon Kornblith, Google. Deep learning, representation learning, transfer learning, analysis of neural network representations, neuroscience. [OpenReview] [Google Scholar]
- Christian Kroer, Columbia University. Game theory, nash equilibrium, market design, auctions, first-order methods game theory, online learning game theory, online learning auctions. [OpenReview] [Google Scholar]
- Alp Kucukelbir, Columbia University. Bayesian inference approximate inference variational inference, probabilistic programming. [OpenReview] [Google Scholar]
- Brian Kulis, Amazon. 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]
- Branislav Kveton, Amazon. Bandits, recommender systems, learning to rank, online learning, markov decision processes, reinforcement learning. [OpenReview] [Google Scholar]
- Simon Lacoste-julien, University of Montreal. Deep learning theory, large scale optimization, convex optimization, structured prediction, topic models. [OpenReview] [Google Scholar]
- Balaji Lakshminarayanan, Google Brain. Probabilistic deep learning, out-of-distribution robustness, deep ensembles and bayesian deep learning, uncertainty quantification and calibration, out-of-distribution detection and open set recognition. [OpenReview] [Google Scholar]
- Marc Lanctot, DeepMind. Computational game theory multiagent learning reinforcement learning planning game tree search. [OpenReview] [Google Scholar]
- Angeliki Lazaridou, DeepMind. Emergent communication, compositionality, multimodal language learning. [OpenReview] [Google Scholar]
- Jaehoon Lee, Google. Deep learning, theoretical physics, machine learning, bayesian deep learning. [OpenReview] [Google Scholar]
- Kangwook Lee, University of Wisconsin, Madison. 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]
- Lihong Li, Amazon. Contextual bandit, reinforcement learning. [OpenReview] [Google Scholar]
- Yingzhen Li, Imperial College London. Continue learning, disentangled representation, gaussian process, meta learning, stochastic gradient mcmc, adversarial attacks and defences, bayesian neural networks, deep generative models (vaes and gans), transfer learning, approximate inference, variational inference, message passing. [OpenReview] [Google Scholar]
- Yujia Li, DeepMind. Program synthesis, deep learning, structured prediction. [OpenReview] [Google Scholar]
- Hsuan-tien Lin, National Taiwan University. Active learning, multi-label learning, cost-sensitive classification. [OpenReview] [Google Scholar]
- Tie-yan Liu, Microsoft. Ai for science, deep learning and reinforcement learning, distributed machine learning, information retrieval and natural language processing, deep learning, reinforcement learning, distributed learning, learning to rank. [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 AI Lab. Deep learning, information retrieval, big data, machine learning, computer vision, pattern recognition. [OpenReview] [Google Scholar]
- Roi Livni, Tel Aviv University. Learning theory. [OpenReview]
- Mingsheng Long, Tsinghua University, Tsinghua University. Continual learning, domain generalization, out-of-distribution generalization, few-shot learning, predictive learning, adversarial learning, spatiotemporal learning, multi-task learning, deep learning, transfer learning, domain adaptation. [OpenReview] [Google Scholar]
- Stephan Mandt, University of California, Irvine. Machine learning for climate science, neural data compression, bayesian deep learning, deep generative models, variational inference, approximate bayesian inference. [OpenReview] [Google Scholar]
- Jérémie Mary, Criteo. Generative adversarial networks, recommender systems, reinforcement learning. [OpenReview] [Google Scholar]
- Laurent Massoulié, INRIA. Distributed optimization federated learning, unsupervised learning graph clustering spectral methods graph alignment, random graphs stochastic block models, probabilistic modeling statistical physics, distributed systems gossip protocols epidemic processes. [OpenReview] [Google Scholar]
- Kuldeep s. Meel, National University of Singapore. Reasoning under uncertainty, sat solving. [OpenReview]
- Nishant 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 Menon, Google. Proper losses surrogate losses classification calibration, long-tail learning class imbalance, distillation label smoothing. [OpenReview]
- Josh Merel, Meta Reality Labs. Computational neuroscience, motor control, imitation learning, reinforcement learning, robotics. [OpenReview] [Google Scholar]
- Andrew Miller, Apple. Probabilistic modeling, variational inference, generative modeling, health, medicine, approximate inference. [OpenReview] [Google Scholar]
- Bamdev Mishra, Microsoft. 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]
- Guido Montufar, UCLA . Deep learning theory, graphical models, exponential families, boltzmann machines, neural networks. [OpenReview] [Google Scholar]
- Shinichi Nakajima, TU Berlin. Generative models, monte carlo sampling, variational inference, bayesian learning. [OpenReview] [Google Scholar]
- Karthik 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]
- Behnam Neyshabur, Google. Capabilities of large language models reasoning, out-of-distribution generalization, deep learning generalization in deep learning implicit regularization, machine learning optimization statistical learning theory, computational biology. [OpenReview] [Google Scholar]
- Gang Niu, RIKEN. Deep learning and representation learning, weakly supervised learning, semi-supervised learning. [OpenReview] [Google Scholar]
- Mohammad Norouzi, Google Brain. Self-supervised learning, generative models. [OpenReview] [Google Scholar]
- Ronald Ortner, Montanuniversitaet Leoben. Markov decision process, regret, reinforcement learning. [OpenReview]
- Ivan Oseledets, Skolkovo Institute of Science and Technology. Deep learning, machine learning, tensor decompositions, numerical linear algebra. [OpenReview] [Google Scholar]
- Dimitris Papailiopoulos, University of Wisconsin - Madison. Distributed systems, coding theory, information theory, machine learning. [OpenReview] [Google Scholar]
- George Papamakarios, DeepMind. Generative models explicit-likelihood models normalizing flows, approximate bayesian inference variational inference simulation-based inference. [OpenReview] [Google Scholar]
- Nicolas Papernot, University of Toronto. Trustworthy machine learning, machine unlearning, privacy-preserving machine learning, adversarial examples. [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]
- Olivier Pietquin, Google Brain. Reinforcement learning, speech processing and natural language. [OpenReview] [Google Scholar]
- Pascal Poupart, University of Waterloo. Reinforcement learning markov decision processes, probabilistic graphical models sum-product networks. [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]
- Liva Ralaivola, Criteo. Optimal transport, bandit algorithms, concentration inequalities, classification noise. [OpenReview] [Google Scholar]
- Marcello Restelli, Politecnico di Milano. Machine learning, reinforcement learning. [OpenReview] [Google Scholar]
- Blake 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]
- Andrej Risteski, Carnegie Mellon University. Deep learning theory deep generative models unsupervised learning out-of-distribution generalization ood generalization, probabilistic graphical models variational inference markov chain monte carlo word embeddings. [OpenReview]
- Marcus Rohrbach, Facebook. Visual question answering, vision and language, computer vision. [OpenReview] [Google Scholar]
- Daniel Roy, Vector Institute. Statistical learning theory, bayesian statistics, online learning, deep learning. [OpenReview] [Google Scholar]
- Francisco 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]
- Purnamrita Sarkar, University of Texas, Austin. Asymptotic statistics spectral methods networks, resampling methods bootstrap subsampling jackknife. [OpenReview]
- 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 Research. Deep learning fairness, health time series deep learning rnns, deep learning explainability, health model interpretation explainability. [OpenReview] [Google Scholar]
- Hanie Sedghi, Google Research, Brain team. Reasoning capabilities of large language models, out of distribution generalization distribution shift, deep learning theory generalization regularization, active learning for tensor methods, method of moments tensor methods spectral methods neural network deep learning rnns theoretical machine learning, optimization stochastic optimization in high dimensions high dimensional statistics convergence bounds, conditional random fields graphical models. [OpenReview] [Google Scholar]
- Nihar Shah, Carnegie Mellon University. Crowdsourcing, ranking, peer review. [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]
- Jonathon Shlens, Apple. Computer vision, deep learning, visual representational learning. [OpenReview] [Google Scholar]
- Vikas Sindhwani, Google. Manifold learning, numerical optimization, robotics control reinforcement learning, semi-supervised learning, kernel methods. [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]
- Sebastian Stich, CISPA Helmholtz Center for Information Security. Federated learning, distributed training parallel learning, decentralized learning, coordinate descent, optimization. [OpenReview] [Google Scholar]
- Dj Strouse, DeepMind. Reinforcement learning deep learning multi-agent reinforcement learning information theory. [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 Tomczak, Vrije Universiteit Amsterdam. Derivative-free optimization, variational inference, deep generative modeling, deep learning, boltzmann machines, ensemble learning svm concept drift decision rules change detection. [OpenReview] [Google Scholar]
- Michal Valko, DeepMind. Self-supervised learning, reinforcement learning, bandits, online learning, semi-supervised learning, graph-based learning, neural networks. [OpenReview] [Google Scholar]
- Rianne Van den berg, Microsoft. Variational inference generative modeling density estimation normalizing flows physics quantum probabilistic modeling. [OpenReview] [Google Scholar]
- Antonio Vergari, University of Edinburgh. Neuro-symbolic reasoning, 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]
- Fabio Viola, DeepMind. Reinforcement learning, deep generative models, computer vision graphics. [OpenReview] [Google Scholar]
- Matthew 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]
- Yu-xiang Wang, UC Santa Barbara. Online learning (dynamic regret / adaptive regret), reinforcement learning (theory), statistical theory and methodology, differential privacy. [OpenReview] [Google Scholar]
- Yunhe Wang, Huawei Noah's Ark Lab. Deep neural networks, machine learning, computer vision. [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 Williamson, University of Texas, Austin. Network models social networks graphs, bayesian nonparametrics, bayesian inference mcmc probabilistic modeling bayesian statistics. [OpenReview] [Google Scholar]
- Ole Winther, Copenhagen University. Deep learning, information retrieval, gaussian processes. [OpenReview] [Google Scholar]
- Jiajun Wu, Stanford University. Robotics, computational cognitive science, computer vision, machine learning. [OpenReview] [Google Scholar]
- Ying nian Wu, UCLA. Representation learning, generative models, unsupervised learning. [OpenReview] [Google Scholar]
- Chang Xu, University of Sydney. Deep neural network design and optimisation, adversarial machine learning and applications, multi-view/label/task learning. [OpenReview] [Google Scholar]
- Makoto Yamada, Kyoto University. Optimal transport, feature selection, multi-task learning, kernel methods. [OpenReview] [Google Scholar]
- Yiming Ying, State University of New York, Albany. Statistical learning theory machine learning optimization differential privacy fairness. [OpenReview] [Google Scholar]
- Yaoliang Yu, University of Waterloo. Optimization, generative models, robustness. [OpenReview] [Google Scholar]
- Manzil Zaheer, Zaheer. Transformers, question answering, semiparametric models, nonconvex optimization, invariance and equivariance in neural networks, efficient ml. [OpenReview] [Google Scholar]
- Chicheng Zhang, University of Arizona. Reinforcement learning theory, bandits, active learning, online learning, learning theory. [OpenReview] [Google Scholar]
- Hanwang Zhang, Nanyang Technological University. Causal inference, scene graph generation, vision-language. [OpenReview] [Google Scholar]
- Lijun Zhang, Nanjing University. Online learning bandits stochastic optimization convex optimization deep learning neural networks, compressive sensing matrix completion sparse learning, dimensionality reduction active learning clustering. [OpenReview] [Google Scholar]
- Qin Zhang, Indiana University. Clustering bandits streaming. [OpenReview] [Google Scholar]
- Qibin Zhao, RIKEN. Tensor networks adversarial machine learning deep learning, tensor decomposition, tensor regression and classification. [OpenReview] [Google Scholar]
- Zhihui Zhu, Ohio State University, Columbus. Deep learning, machine learning, optimization, signal processing. [OpenReview] [Google Scholar]

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