TMLR Editorial Board

Editors-in-Chief

• Gautam Kamath, University of Waterloo.
• Hugo Larochelle, Mila and Google DeepMind.
• Naila Murray, Meta AI.
• Nihar B. Shah, CMU.

Past Editors-in-Chief

• Kyunghyun Cho, Genentech and New York University.
• Raia Hadsell, Google DeepMind.

Managing Editors

• Paul Vicol, Google DeepMind.

Past Managing Editors

• Fabian Pedregosa, Google DeepMind.

TMLR Action Editors

• Pierre Ablin, Apple. Invertible neural networks, sparsity, optimization, manifolds. [OpenReview] [Google Scholar]
• Yossi Adi, Hebrew University of Jerusalem. Machine learning deep learning structured prediction speech language models speech and audio processing speech recognition speech synthesis. [OpenReview] [Google Scholar]
• Sungsoo Ahn, Korea Advanced Institute of Science & Technology. Machine learning for molecules. [OpenReview] [Google Scholar]
• Sai Aparna Aketi, Meta. Privacy preserving machine learning, federated learning, decentralized optimization, radiation hardening by design techniques. [OpenReview] [Google Scholar]
• Ahmet Alacaoglu, University of British Columbia. Reinforcement learning, variational inequalities and monotone operator theory, nonconvex optimization, stochastic algorithms, convex optimization, algorithms for min-max problems. [OpenReview] [Google Scholar]
• Alexander A Alemi, Google Deepmind. Variational information bottleneck, deep learning, information theory. [OpenReview] [Google Scholar]
• Rahaf Aljundi, Toyota Motor Europe. Vision language models privacy leakage memory construction personalization, lifelong learning incremental learning domain adaptation ood zero shot methods. [OpenReview] [Google Scholar]
• Mauricio A Álvarez, University of Manchester. Gaussian processes non-parametric bayes dynamical systems kernel methods. [OpenReview] [Google Scholar]
• Ehsan Amid, Google DeepMind. Online learning, machine learning. [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]
• Elahe Arani, Eindhoven University of technology . Continual learning, self-supervised learning, representation learning, generative ai, multimodal learning, neuroscience, brain inspired artificial intelligence, computer vision, machine learning and deep learning. [OpenReview] [Google Scholar]
• Adam Arany, KU Leuven. Causality time series, bayesian methods uncertainty quantification, odes neural ode. [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]
• Oleg Arenz, TU Darmstadt. Variational inference, inverse reinforcement learning, imitation learning, robotics, reinforcement learning. [OpenReview] [Google Scholar]
• Anurag Arnab, Google. Transformer, video, 3d human pose, deep learning, computer vision, segmentation. [OpenReview] [Google Scholar]
• Kamyar Azizzadenesheli, NVIDIA. Reinforcement learning learning theory optimization applied mathematics neural operators. [OpenReview] [Google Scholar]
• Reza Babanezhad Harikandeh, Samsung. Neural network, continous optimization. [OpenReview] [Google Scholar]
• Yu Bai, OpenAI. Deep learning theory, uncertainty quantification, game theory, machine learning theory, reinforcement learning, large language models. [OpenReview] [Google Scholar]
• Han Bao, Kyoto University, Kyoto University. Learning theory, adversarial robustness, representation learning, self-supervised learning, loss function. [OpenReview] [Google Scholar]
• Serguei Barannikov, Skolkovo Institute of Science and Technology. Topological data analysis, topology, persistent homology, generative models, generative adversarial models, attention graphs, bert, representations learning, representations, disentanglement, autoencoders, variational autoencoders, loss landscape, loss surfaces, attention mechanism, 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]
• Amrit Singh Bedi, University of Central Florida. Ai alignment language models reinforcement learning policy gradient algorithms. [OpenReview] [Google Scholar]
• Ahmad Beirami, Google. Natural language processing, conversational ai, language models, responsible ai, game ai, reinforcement learning, machine learning, information theory, statistics, signal processing, federated learning. [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]
• Emmanuel Bengio, Recursion. Machine learning deep learning reinforcement learning. [OpenReview] [Google Scholar]
• Jonathan Berant, Google. Natural language processing. [OpenReview] [Google Scholar]
• Michel Besserve, Technische Universität Carolo-Wilhelmina zu Braunschweig. Causality, kernel methods, neuroscience, random matrix theory, information theory, generative models. [OpenReview] [Google Scholar]
• Srinadh Bhojanapalli, Google. Generalization, optimization, matrix factorization, deep learning. [OpenReview] [Google Scholar]
• Jiang Bian, Microsoft. Llm for decision making generative ai for simulation ai for industrial applications. [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]
• Soma Biswas, Indian Institute of Science, Bangalore, India. Computer vision deep learning cross-modal retrieval face recognition incremental learning. [OpenReview]
• Michael Bowling, Department of Computing Science, University of Alberta. Multiagent learning, game theory, reinforcement learning. [OpenReview] [Google Scholar]
• Vladimir Braverman, UT Health. Algorithms, clustering, big data science &engineering cyberinfra frmwrk 21st (cif21) big data science &engineering, federated learning, health ai, continual learning, compression, attention. [OpenReview] [Google Scholar]
• Marcus A Brubaker, Google DeepMind. Normalizing flows, electron cryomicroscopy, monte carlo methods, machine learning, probabilistic programming, computer vision, texture synthesis, human motion estimation, physics-based human motion models. [OpenReview] [Google Scholar]
• Marlos C. Machado, University of Alberta. Reinforcement learning, representation learning, hierarchical reinforcement learning, exploration, continual learning. [OpenReview] [Google Scholar]
• Diana Cai, Flatiron Institute. Bayesian inference, bayesian nonparametrics, variational inference, mcmc, gaussian processes, bayesian active learning. [OpenReview] [Google Scholar]
• Trevor Campbell, University of British Columbia. Statistical machine learning, probability, bayesian statistics, large-scale data. [OpenReview]
• Xiaochun Cao, SUN YAT-SEN UNIVERSITY. Adversarial learning, segmentation parsing, image enhancement, detection, clustering, recognition. [OpenReview] [Google Scholar]
• Yuan Cao, University of Hong Kong. Deep learning neural networks graphical model. [OpenReview]
• Olivier Cappé, CNRS. [OpenReview] [Google Scholar]
• Gustavo Carneiro, University of Surrey. Noisy label learning, breast image analysis, deep learning cnns rnns, medical image analysis, meta learning, few-shot learning. [OpenReview] [Google Scholar]
• Wilka Carvalho, Harvard University, Harvard University. Factored mpds, reinforcement learning, representation learning, object-centric. [OpenReview] [Google Scholar]
• Pablo Samuel Castro, Google. Reinforcement learning. [OpenReview] [Google Scholar]
• Anthony L. Caterini, Layer6. Tabular foundation models, tabular data, deep generative models, normalizing flows, variational inference. [OpenReview] [Google Scholar]
• Philip K. Chan, Florida Institute of Technology. Self-supervised learning, representation learning, machine learning. [OpenReview] [Google Scholar]
• Sarath Chandar, École Polytechnique de Montréal. Optimization, ai for science, lifelong/continual learning, deep learning, natural language processing, reinforcement learning. [OpenReview] [Google Scholar]
• Shiyu Chang, University of California, 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]
• 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]
• Chao Chen, State University of New York at Stony Brook. Microscopic image, digital pathology, medical image analysis, topological data analysis, persistent homology. [OpenReview] [Google Scholar]
• Liang-Chieh Chen, ByteDance / TikTok. Computer vision semantic segmentation panoptic segmentation instance segmentation scene parsing video segmentation deep learning architectures representation learning vision-language model design open-vocabulary segmentation. [OpenReview] [Google Scholar]
• Pin-Yu Chen, International Business Machines. Adversarial machine learning, trustworthy machine learning, adversarial robustness, machine learning and security, ai safety and alignment. [OpenReview] [Google Scholar]
• Xinlei Chen, Meta. Self-supervised learning, unsupervised learning, visual question answering, instance segmentation, object detection, embodied agent, context reasoning. [OpenReview] [Google Scholar]
• Yutian Chen, DeepMind. Gaussian process, bayesian, meta-learning, mcmc, speech, generative model, reinforcement learning, blackbox optimization, bayesian optimization, diffusion. [OpenReview] [Google Scholar]
• Yu Cheng, The Chinese University of Hong Kong. Natural language processing, deep learning, machine learning, computer vision. [OpenReview] [Google Scholar]
• Seungjin Choi, Intellicode. Bayesian optimization, uncertainty quantification, probabilistic models, meta-learning, distribution shift. [OpenReview] [Google Scholar]
• Grigorios Chrysos, University of Wisconsin - Madison. Polynomial neural networks tensor decompositions, extrapolation, unsupervised learning representation learning, generative models. [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]
• Kamil Ciosek, Spotify. [OpenReview]
• Mark J. Coates, McGill University. Recommender systems, graph learning, bayesian inference, particle filters. [OpenReview] [Google Scholar]
• Nadav Cohen, School of Computer Science, Tel Aviv University. Learning for control, statistical learning theory deep learning non-convex optimization tensor analysis. [OpenReview] [Google Scholar]
• Shay B. Cohen, University of Edinburgh. Natural language processing. [OpenReview]
• Yin Cui, NVIDIA. Computer vision, machine learning. [OpenReview] [Google Scholar]
• Jacek Cyranka, Aptiv. Reinforcement learning, time series forecasting, topological data analysis, partial differential equations, computer assisted proofs, dynamical systems. [OpenReview] [Google Scholar]
• Bo Dai, Georgia Institute of Technology. Reinforcement learning, probabilistic method, kernel method. [OpenReview] [Google Scholar]
• Yuchao Dai, Northwestern Polytechnical University. Novel view synthesis, rolling shutter, event camera, dynamic reconstruction, computer vision deep learning multiview geometry camera, structure from motion 3d reconstruction. [OpenReview] [Google Scholar]
• Valentin De Bortoli, University of Oxford. Stochastic optimization, texture synthesis, information geometry, generative modeling, diffusion model, score-matching, schrodinger bridges. [OpenReview]
• Weijian Deng, Australian National University. 3d content modeling; machine learning safety; monitoring model reliability, trustworthy machine learning, model generalization; unsupervised accuracy estimation;, generative adversarial network; fine-grained recognition. [OpenReview] [Google Scholar]
• Sameer Deshpande, University of Wisconsin - Madison. Bayesian additive regression trees, bayesian variable selection, causal inference, graphical modeling, hierarchical modeling. [OpenReview] [Google Scholar]
• Devendra Singh Dhami, Eindhoven University of Technology. Neuro-symbolic artificial intelligence, causal machine learning, machine learning in healthcare, time series forecasting. [OpenReview] [Google Scholar]
• Laurent Dinh, Apple. Deep learning, unsupervised learning, generative models, deep invertible models, flow based models, probabilistic inference. [OpenReview] [Google Scholar]
• Jose Dolz, École de technologie supérieure. Vision language models, network calibration, image segmentation, weakly supervised learning. [OpenReview] [Google Scholar]
• Li Dong, Microsoft Research. Deep learning, natural language processing. [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 at 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. Model merging, unlearning, data compression, neural network pruning, generalization theory. [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]
• Marwa El Halabi, Samsung. Pruning neural networks, submodular optimization, structured sparsity, convex optimization. [OpenReview] [Google Scholar]
• Moshe Eliasof, University of Cambridge. Graph neural networks, deep learning, computer graphics, computer vision, signal and image processing. [OpenReview] [Google Scholar]
• Murat A Erdogdu, University of Toronto. Optimization, sampling. [OpenReview] [Google Scholar]
• Amir-massoud Farahmand, Polytechnique Montréal. Reinforcement learning statistical learning theory nonparametric estimators. [OpenReview] [Google Scholar]
• Aleksandra Faust, Google Brain. Self-improvement, foundation models, natural language processing, meta-learning, task learning, autonomous driving, navigation, reinforcement learning, motion planning, robotics. [OpenReview] [Google Scholar]
• Lei Feng, Singapore University of Technology and Design. Foundation models, trustworty deep learning, weakly supervised learning. [OpenReview] [Google Scholar]
• Stefan Feuerriegel, LMU Munich. Computational social science. [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]
• Chuan Sheng Foo, Centre for Frontier AI Research, A*STAR. Anomaly detection, data-efficient learning, robust deep learning, computational biology bioinformatics. [OpenReview] [Google Scholar]
• Vincent Fortuin, Technical University of Munich. Bayesian deep learning, gaussian processes, deep learning, machine learning. [OpenReview] [Google Scholar]
• David Fouhey, Carnegie Mellon University. Computer vision 3d reconstruction 3d from a single image, computer vision human-object interaction affordances, ai for science. [OpenReview] [Google Scholar]
• Jes Frellsen, Technical University of Denmark. Missing data, deep generative models, deep learning, bayesian modelling and inference, markov chain monte carlo, bioinformatics. [OpenReview] [Google Scholar]
• Yanwei Fu, Fudan University,. Image inpainting, robotic grasping, sparsity in neural network, learning based 3d reconstruction, facial analysis and person understanding, zero-shot learning and attribute learning, few-shot 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]
• Maxime Gasse, ServiceNow. Causality, reinforcement learning, combinatorial optimization, structure learning, probabilistic graphical models directed acyclic graphs. [OpenReview] [Google Scholar]
• Stratis 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]
• Bernhard C Geiger, Technische Universität Graz. Information theory, deep learning, model reduction. [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 AGI. Rlhf, bandit algorithms, online learning, reinforcement learning. [OpenReview] [Google Scholar]
• Surbhi Goel, University of Pennsylvania. Theory, machine learning. [OpenReview] [Google Scholar]
• Vicenç Gómez, Universitat Pompeu Fabra. Optimal control, reinforcement learning, probabilistic graphical models, social networks. [OpenReview] [Google Scholar]
• Boqing Gong, Boston University, Boston University. Machine learning, computer vision. [OpenReview] [Google Scholar]
• Mingming Gong, University of Melbourne. Deep generative models, 3d vision, causal inference. [OpenReview] [Google Scholar]
• Eduard Gorbunov, Mohamed bin Zayed University of Artificial Intelligence. Optimization, machine learning, stochastic optimization, distributed optimization, derivative-free optimization, federated learning, variational inequalities, min-max problems. [OpenReview] [Google Scholar]
• Robert M. Gower, Flatiron Institute. Adaptive gradient methods;, learning rate schedules, 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]
• Magda Gregorova, Technical University of Applied Sciences Würzburg-Schweinfurt. Diffusion models classifier guidance, generative models vae invertible flows, nonlinear regression rkhs learning, structured sparsity regularised learning. [OpenReview] [Google Scholar]
• Quanquan Gu, ByteDance Inc.. Large language models, deep generative models, ai for science, reinforcement learning, optimization, deep learning, high-dimensional statistics, active learning, online learning. [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, EPFL - EPF Lausanne. Large language models, foundation models, 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]
• Chuan Guo, Meta. Privacy attacks, differential privacy, adversarial example, ai security, model calibration. [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]
• John Timothy Halloran, Leidos. Efficient llm training and inference, privacy-preserving machine learning, deep generative modeling, generative time-series modeling. [OpenReview] [Google Scholar]
• Bo Han, HKBU. Trustworthy machine learning, deep learning, machine 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]
• Satoshi Hara, University of Electro-Communications. Interpretability, anomaly detection, feature selection. [OpenReview] [Google Scholar]
• Tatsuya Harada, RIKEN. Deep learning, visual recognition. [OpenReview] [Google Scholar]
• Adam W Harley, Meta. Deep learning computer vision point tracking. [OpenReview] [Google Scholar]
• Søren Hauberg, Technical University of Denmark. Bayesian deep learning, gaussian processes, manifold learning, differential geometry, human motion capture. [OpenReview] [Google Scholar]
• Manuel Haussmann, University of Southern Denmark - SDU. Bayesian statistics, bayesian deep learning, probabilistic machine learning, reinforcement learning. [OpenReview] [Google Scholar]
• Di He, Peking University. Graph neural networks, geometric neural networks, natural language processing. [OpenReview] [Google Scholar]
• Xuming He, ShanghaiTech University. Continual learning; novel class discovery, visual relation detection; scene graph generation, multimodal learning, semantic segmentation scene parsing object segmentation. [OpenReview] [Google Scholar]
• Chinmay Hegde, New York University. Llm benchmarking, ai safety, multimodal models, privacy in machine learning, physics-aware machine learning, robustness of machine learning, neural architecture search, theory of neural network learning. [OpenReview] [Google Scholar]
• Markus Heinonen, Aalto University. Gaussian process, deep learning, kernel methods, differential equations. [OpenReview] [Google Scholar]
• Derek Hoiem, University of Illinois at Urbana-Champaign. Computer vision scene understanding. [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]
• Antti Honkela, University of Helsinki. Differential privacy, privacy, bayesian machine learning, bioinformatics, gaussian processes. [OpenReview] [Google Scholar]
• Jean Honorio, University of Melbourne. Learning theory, planted models, graphical models, structured prediction, community detection. [OpenReview] [Google Scholar]
• Neil Houlsby, Google. Computer vision, natural language processing, machine learning. [OpenReview] [Google Scholar]
• Yen-Chang Hsu, Samsung Research America. Out-if-distribution detection anomaly detection confidence estimation, computer vision semantic segmentation, deep learning machine learning clustering object discovery supervised learning unsupervised learning semi-supervised learning self-supervised learning continual learning, knowledge distillation model compression model pruning model editing, speculative decoding, language modeling. [OpenReview] [Google Scholar]
• Jia-Bin Huang, University of Maryland, College Park. Machine learning, computer vision. [OpenReview] [Google Scholar]
• Kejun Huang, University of Florida. [OpenReview] [Google Scholar]
• W Ronny Huang, Google. Speech, large language models. [OpenReview] [Google Scholar]
• Wen-bing Huang, Renmin University of China. Ai for science, geometric deep learning, graph neural networks. [OpenReview] [Google Scholar]
• Yani Ioannou, University of Calgary. Efficient deep learning, grouped convolution, unstructured sparse neural networks, dynamic sparse neural network training, sparse neural network training, neural network pruning, lottery ticket hypothesis. [OpenReview] [Google Scholar]
• Daphne Ippolito, School of Engineering and Applied Science, University of Pennsylvania. [OpenReview]
• Takashi Ishida, RIKEN. Bayes error estimation, overfitting regularization, semi-supervised learning weakly supervised learning. [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]
• Pavel Izmailov, Anthropic. Ai alignment, reasoning, language models, spurious correlations, robustness, out-of-distribution generalization, interpretability, deep learning, bayesian deep learning, bayesian machine learning, normalizing flows, semi-supervised learning, gaussian processes. [OpenReview] [Google Scholar]
• Shahin Jabbari, Drexel University. [OpenReview] [Google Scholar]
• Joonas Jälkö, University of Helsinki. Differential privacy bayesian inference variational inference. [OpenReview]
• Stephen James, Dyson. Robotics, sim2real, reinforcement learning, imitation learning. [OpenReview] [Google Scholar]
• 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]
• C.V. Jawahar, IIIT Hyderabad. Computer vision. [OpenReview] [Google Scholar]
• Dinesh Jayaraman, University of Pennsylvania, University of Pennsylvania. Reinforcement learning, robotics, robot learning, embodied ai, unsupervised feature learning, visual recognition. [OpenReview] [Google Scholar]
• Shuiwang Ji, Texas A&M University. Physics simulation, ai for science, protein modeling, drug discovery, fluid flow simulations, quantum mechanics, density functional theory, materials discovery, equivariant graph networks. [OpenReview] [Google Scholar]
• Jing Jiang, University of Technology Sydney. Federated learning. [OpenReview] [Google Scholar]
• Lu Jiang, ByteDance Inc.. Multimodal foundation modal, video generation, image synthesis, robust machine learning, video understanding. [OpenReview] [Google Scholar]
• Jianbo Jiao, University of Birmingham. Medical imaging, multi-modal learning and understanding, self-supervised learning, low-level vision, 3d vision. [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]
• Samira Ebrahimi Kahou, University of Calgary. Deep learning computer vision model compression emotion recognition attention. [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]
• Dileep Kalathil, Texas A&M University. Reinforcement learning. [OpenReview] [Google Scholar]
• Varun Kanade, University of Oxford. Optimization, deep learning, randomized algorithms, random graphs, computational learning theory. [OpenReview]
• Dmitry Kangin, University of Manchester. Interpretable machine learning, continuous neural networks, generative models, deep reinforcement learning, multiple target tracking, machine vision. [OpenReview] [Google Scholar]
• Ian A. Kash, University of Illinois, Chicago. Algorithmic fairness, multi-agent reinforcement learning, economics and computaiton. [OpenReview] [Google Scholar]
• Yoshinobu Kawahara, Osaka University. Machine learning, dynamical systems. [OpenReview] [Google Scholar]
• Mohammad Emtiyaz Khan, RIKEN Center for AI Project. Approximate inference, bayesian deep learning, gaussian processes, continual learning, lifelong learning, adaptation, variational inference, distributed learning. [OpenReview] [Google Scholar]
• Niki Kilbertus, Technische Universität München. [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]
• Gunhee Kim, Seoul National University. Deep learning, computer vision. [OpenReview] [Google Scholar]
• 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]
• 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, University of Leipzig. Bayesian optimization hyperparameter optimization neural-architecture search automl. [OpenReview] [Google Scholar]
• Junpei Komiyama, New York University. Reproducibility, online learning, multi-armed bandit problem, algorithmic fairness. [OpenReview] [Google Scholar]
• Lingpeng Kong, Department of Computer Science, The University of Hong Kong. Machine learning nlp. [OpenReview] [Google Scholar]
• Alec Koppel, J.P. Morgan Chase. Online learning, reinforcement learning markov decision processes, kernel methods, supervised learning, stochastic optimization. [OpenReview] [Google Scholar]
• Simon Kornblith, Anthropic. Large language model training, vision language models, deep learning, representation learning, transfer learning, analysis of neural network representations, neuroscience. [OpenReview] [Google Scholar]
• Antti Koskela, Nokia Bell Labs. Differential privacy, machine learning, deep learning, numerical linear algebra. [OpenReview] [Google Scholar]
• Jean Kossaifi, NVIDIA AI. [OpenReview] [Google Scholar]
• Florent Krzakala, Swiss Federal Institute of Technology Lausanne. Statistical physics, high-dimensional asymptotics, universality, statistical learning theory, dynamics of sgd, replica method. [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]
• Weicheng Kuo, Google Deepmind. Object detection multimodal learning visual grounding language modeling. [OpenReview]
• Branislav Kveton, Adobe Research. 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, mixture of expers, distributed computing, quantum computing. [OpenReview] [Google Scholar]
• Andrew Lampinen, Google DeepMind. Representation analysis interpretability, reinforcement learning, zero-shot learning adaptation zero shot transfer flexibility, natural language processing, memory episodic memory complementary learning systems, multi-task learning transfer curricula, cognitive science cognition. [OpenReview] [Google Scholar]
• Marc Lanctot, Google DeepMind. Computational game theory multiagent learning reinforcement learning planning game tree search. [OpenReview] [Google Scholar]
• Romain Laroche, Microsoft. Reinforcement learning. [OpenReview] [Google Scholar]
• Sylvain Le Corff, Sorbonne Université, LPSM. Simulation methods for generative models, probabilistic machine learning, hidden markov models, sequential monte carlo, markov chain monte carlo, bayesian nonparametric. [OpenReview] [Google Scholar]
• Antoine Ledent, Singapore Management University. Matrix completion, statistical learning theory, interpretability regularisation, stochastic analysis. [OpenReview] [Google Scholar]
• Hankook Lee, Sungkyunkwan University. Transfer learning meta-learning, self-supervised learning representation learning. [OpenReview] [Google Scholar]
• Jaehoon Lee, Anthropic. Deep learning, theoretical physics, machine learning. [OpenReview] [Google Scholar]
• Jasper C.H. Lee, University of California, Davis. Discrete optimization machine learning, learning theory. [OpenReview] [Google Scholar]
• Kangwook Lee, University of Wisconsin, Madison. Large language models, diffusion models, deep learning, information theory, fairness in machine learning, distributed machine learning. [OpenReview] [Google Scholar]
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