TMLR Expert Reviewers

2025 Expert Reviewers

Atish Agarwala, Google. Deep learning theory, theoretical biology, evolution, learning dynamics. [OpenReview] [Google Scholar]
James Urquhart Allingham, Google DeepMind. Bayesian deep learning, deep generative models. [OpenReview] [Google Scholar]
Julius Berner, NVIDIA. Neural operators, generative modeling, sampling, approximation theory, deep learning, statistical learning theory, machine learning, partial differential equations, applied mathematics, numerical analysis. [OpenReview] [Google Scholar]
Michael Boratko, Google. Core machine learning deep learning optimization graph modeling. [OpenReview] [Google Scholar]
Neil Burch, Sony AI. Multiagent learning, game theory, search. [OpenReview] [Google Scholar]
Dan Busbridge, Apple. Deep learning, rnns, distributed representations, theoretical physics, particle physics. [OpenReview] [Google Scholar]
Luca Butera, Universita della Svizzera Italiana. Computer vision, graph deep learning. [OpenReview] [Google Scholar]
Angel X Chang, Simon Fraser University. Text to scene generation, text to shape generation, 3d scene understanding, multimodal reasoning, grounded language, embodied ai, referring expressions. [OpenReview] [Google Scholar]
Mark J. Coates, McGill University. Recommender systems, graph learning, bayesian inference, particle filters. [OpenReview] [Google Scholar]
Alessandro Conti, University of Trento. Vocabulary-free image classification open-world image classification, large multimodal models vision and language models, domain adaptation source-free domain adaptation emotion recognition. [OpenReview] [Google Scholar]
Ian Connick Covert, Stanford University. Multi-modality, amortization, interpretability, feature selection. [OpenReview] [Google Scholar]
Guillaume Dalle, Ecole Nationale des Ponts et Chausees. Combinatorial optimization, differentiable programming, julia, graphs, graph machine learning, automatic differentiation, operations research. [OpenReview] [Google Scholar]
Alessandro De Palma, London School of Economics and Political Science, University of London. Neural network verification, optimisation for machine learning, trustworthy machine learning, adversarial machine learning. [OpenReview] [Google Scholar]
Alexander S. Ecker, Georg-August Universität Göttingen. Deep learning cnns classification segmentation, visual perception neuroscience neuroscience-inspired machine learning. [OpenReview] [Google Scholar]
Tongtong Fang, The Institute of Statistical Mathematics, Japan. Weakly supervised learning, deep learning, transfer learning. [OpenReview] [Google Scholar]
Farzan Farnia, The Chinese University of Hong Kong. Machine learning, information theory. [OpenReview] [Google Scholar]
Ilyas Fatkhullin, ETHZ - ETH Zurich. Reinforcement learning, statistics, optimization, machine learning. [OpenReview] [Google Scholar]
Gergely Flamich, Imperial College London. Algorithmic information theory; kolmogorov complexity, point processes poisson processes, compression image compression learned compression, entropy coding relative entropy coding channel simulation reverse channel simulation. [OpenReview] [Google Scholar]
Billy Joe Franks, Rheinland-Pfälzische Technische Universität. Graph neural networks, anomaly detection. [OpenReview] [Google Scholar]
Jason Alan Fries, Stanford University. Large language models, weak supervision, healthcare, representation learning, natural language processing. [OpenReview] [Google Scholar]
Nicolas Gontier, Servicenow Research. Deep learning transformers rnns chatbots natural language processing systematic generalization agents. [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]
Luigi Gresele, University of Copenhagen. Independent component analysis, causal inference, normalizing flows and invertible neural networks, unsupervised learning, representation learning, probabilistic modelling, causal representation learning, causality, identifiability. [OpenReview] [Google Scholar]
Dong Guo, ByteDance Inc.. Multimodal video understanding, machine learning, automatic speech recognition, graph representation learning. [OpenReview] [Google Scholar]
Manuel Haussmann, University of Southern Denmark - SDU. Bayesian statistics, bayesian deep learning, probabilistic machine learning, reinforcement learning. [OpenReview] [Google Scholar]
Mikko A. Heikkilä, University of Helsinki. Federated learning, privacy differential privacy privacy-preserving machine learning, bayesian inference bayesian machine learning. [OpenReview]
Marek Herde, Universität Kassel. Noisy labels, learning from crowds, uncertainty quantification, active learning. [OpenReview] [Google Scholar]
David I. Inouye, Purdue University. Distribution shift explainable ai interpretability explanability, causal ml counterfactuals causal representation learning counterfactual fairness, federated learning localized learning iterative learning methods, distribution matching domain-invariant representation learning distribution shift. [OpenReview] [Google Scholar]
Daniel D. Johnson, Transluce. Uncertainty quantification, decision theory, language models, variational methods, transformers, implicit differentiation, program synthesis, programming languages, continuous relaxation, bayesian inference, probability theory, graph neural networks, structured data, deep learning, neural networks, rnns. [OpenReview] [Google Scholar]
Christian Keup, University of Parma. Adaptive kernels kernel renormalization feature learning, inference learning energy based models, feature learning theory, active inference neuroai, predictive coding variational inference, statistical physics high dimensional learning bayesian, data representation deep network geometry, spiking networks biologically plausible learning, reservoir computing chaos, field theory rnns, renormalization learning. [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]
Dmitry Kobak, Eberhard-Karls-Universität Tübingen. Contrastive learning, manifold learning, single-cell transcriptomics, neighbor embeddings, dimensionality reduction. [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]
Hanna Krasowski, University of California, Berkeley. Safe reinforcement learning, formal methods, motion planning, systems biology, automomous vessels. [OpenReview] [Google Scholar]
Adhiguna Kuncoro, DeepMind. Deep learning, natural language processing, structured prediction. [OpenReview] [Google Scholar]
Markus Lange-Hegermann, OWL University of Applied Sciences and Arts. Time series, anomaly detection, active learning, gaussian processes, differential equations, algebra. [OpenReview] [Google Scholar]
Tatiana Likhomanenko, Apple. Deep learning speech recognition nlp image recognition, deep learning face recognition image recognition video, high energy physics gradient boosting optimization model compression loss functions. [OpenReview] [Google Scholar]
Gabriel Loaiza-Ganem, Layer 6 AI. Probabilistic machine learning, deep generative models, bayesian methods, variational inference, manifold learning. [OpenReview] [Google Scholar]
Wesley J Maddox, Jump Trading. Deep learning, probabilistic models, gaussian processes, bayesian deep learning. [OpenReview]
Loic Matthey, DeepMind. Deep rl model-based rl exploration, generative models vae. [OpenReview] [Google Scholar]
Aaron Mishkin, Computer Science Department, Stanford University. Optimization convex optimization optimization for deep learning. [OpenReview] [Google Scholar]
Ramchandran Muthukumar, Johns Hopkins University. Generalization theory, adverarial robustness, optimization. [OpenReview] [Google Scholar]
Mirco Mutti, Technion - Israel Institute of Technology. Reinforcement learning. [OpenReview] [Google Scholar]
Kim Andrea Nicoli, Rheinische Friedrich-Wilhelms Universität Bonn. Quantum computing, quantum field theory, generative models, gaussian processes, statistical mechanics, monte carlo sampling. [OpenReview] [Google Scholar]
Theo X. Olausson, Massachusetts Institute of Technology. Attention mechanisms, program synthesis, large language models, neurosymbolic ai, deep learning. [OpenReview] [Google Scholar]
Nived Rajaraman, University of California Berkeley. Matrix sensing and matrix factorization, reinforcement learning. [OpenReview] [Google Scholar]
Parikshit Ram, International Business Machines. Hyperdimensional computing, compositional generalization, meta-learning, bilevel optimization, automated machine learning, large scale learning, decision trees, density estimation, clustering, nearest neighbour search. [OpenReview] [Google Scholar]
Fabian Schaipp, INRIA. Optimization, deep learning, sparse network estimation. [OpenReview]
Aaron Schein, University of Chicago. [OpenReview] [Google Scholar]
Oleksandr Shchur, Amazon. Time series forecasting, temporal point processes, generative models. [OpenReview] [Google Scholar]
Egor Shulgin, KAUST. Federated learning, communication compression for distributed learning, stochastic gradient descent. [OpenReview] [Google Scholar]
Andreas Peter Steiner, Google DeepMind. Deep learning computer vision, whole sequence genome analysis data processing in resource constrained settings. [OpenReview]
Hong Ye Tan, University of California, Los Angeles. Optimization, machine learning, sampling, imaging. [OpenReview] [Google Scholar]
Jacopo Teneggi, Johns Hopkins University. Uncertainty quantification, interpretability. [OpenReview] [Google Scholar]
Vimal Thilak, Apple. Computer vision, machine learning, image processing. [OpenReview] [Google Scholar]
Ran Tian, Google. [OpenReview]
Sebastian Trimpe, RWTH Aachen University. Bayesian optimization, model-based reinforcement learning, control theory, dynamical systems, foundation models control, foundation models dynamical systems, learning-based control, gaussian processes. [OpenReview] [Google Scholar]
George Trimponias, Intercom. Deep learning reinforcement learning automl mixture models multi-objective optimization stochastic approximation combinatorial optimization submodular optimization, machine learning. [OpenReview] [Google Scholar]
Austin Tripp, Valence Labs powered by recursion. Generative models, probabilistic models, gaussian process, optimization molecules, generative models molecules chemistry. [OpenReview] [Google Scholar]
Iñigo Urteaga, Basque Center for Applied Mathematics. Multi-armed bandits thompson sampling reinforcement learning, bayesian theory signal processing statistical signal processing, sequential monte carlo particle filtering monte carlo methods variational inference, probabilistic graphical models generative models, dynamical systems state-space modeling time-series arma models. [OpenReview] [Google Scholar]
Emile van Krieken, Vrije Universiteit Amsterdam. Deep learning automatic differentiation gradient estimation, reinforcement learning policy gradient, neuro-symbolic ai differentiable logics fuzzy logics. [OpenReview] [Google Scholar]
Harley Wiltzer, Mila. Distributional reinforcement learning risk-sensitive control, reinforcement learning control exploration planning. [OpenReview]
Pengzhou Wu, Kyoto University. Causal inference, causal discovery, generative models. [OpenReview] [Google Scholar]
Stanley Wu, University of Chicago. Adversarial machine learning, privacy. [OpenReview] [Google Scholar]
Yi-Shan Wu, University of Southern Denmark - SDU. Reinforcement learning, pac-bayesian large-deviation theory, online learning. [OpenReview] [Google Scholar]
Kai Yan, University of Illinois at Urbana-Champaign. Large language model, imitation learning, reinforcement learning, optimization. [OpenReview] [Google Scholar]
Çağatay Yıldız, Eberhard-Karls-Universität Tübingen. Continual learning, variational inference continual learning neural odes. [OpenReview] [Google Scholar]
Lee Zamparo, InstaDeep. Computational biology deep learning, explainablity text to code active learning model uncertainty. [OpenReview] [Google Scholar]
Huangjie Zheng, Apple. Deep bayesian learning, generative models, representation learning. [OpenReview] [Google Scholar]
Shu Zou, Australian National University. Machine learning, computer vision, model robustness. [OpenReview] [Google Scholar]

2024 Expert Reviewers

Sai Aparna Aketi, Meta. Privacy preserving machine learning, federated learning, decentralized optimization, radiation hardening by design techniques. [OpenReview] [Google Scholar]
Han Bao, The Institute of Statistical Mathematics. Learning theory, adversarial robustness, representation learning, self-supervised learning, loss function. [OpenReview] [Google Scholar]
Emmanuel Bengio, Recursion. Machine learning deep learning reinforcement learning. [OpenReview] [Google Scholar]
Michael Boratko, Google. Core machine learning deep learning optimization graph modeling. [OpenReview] [Google Scholar]
Neil Burch, Sony AI. Multiagent learning, game theory, search. [OpenReview] [Google Scholar]
Mark J. Coates, McGill University. Recommender systems, graph learning, bayesian inference, particle filters. [OpenReview] [Google Scholar]
Ian Connick Covert, Stanford University. Multi-modality, amortization, interpretability, feature selection. [OpenReview] [Google Scholar]
Gergely Flamich, Imperial College London. Algorithmic information theory; kolmogorov complexity, point processes poisson processes, compression image compression learned compression, entropy coding relative entropy coding channel simulation reverse channel simulation. [OpenReview] [Google Scholar]
Maxime Gasse, Microsoft. 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]
Tim Genewein, DeepMind. Meta-learning, ai safety, bayesian deep learning, information theory, rate-distortion theory, analysis and understanding of agents, variational inference, network compression, bounded rationality, computational neuroscience, sensorimotor learning. [OpenReview] [Google Scholar]
Steven Hansen, DeepMind. Intrinsic motivation, unsupervised reinforcement learning, deep reinforcement learning, meta reinforcement learning, model-based reinforcement learning. [OpenReview] [Google Scholar]
Manuel Haussmann, University of Southern Denmark - SDU. Bayesian statistics, bayesian deep learning, probabilistic machine learning, reinforcement learning. [OpenReview] [Google Scholar]
Xuming He, ShanghaiTech University. Continual learning; novel class discovery, visual relation detection; scene graph generation, multimodal learning, semantic segmentation scene parsing object segmentation. [OpenReview] [Google Scholar]
Markus Heinonen, Aalto University. Gaussian process, deep learning, kernel methods, differential equations. [OpenReview] [Google Scholar]
Matthew J. Holland, Kansai University. Learning theory. [OpenReview] [Google Scholar]
Takashi Ishida, The University of Tokyo. Weakly supervised learning, data evaluation, language model alignment, rlhf, scalable oversight, data contamination. [OpenReview] [Google Scholar]
Daniel D. Johnson, Transluce. Uncertainty quantification, decision theory, language models, variational methods, transformers, implicit differentiation, program synthesis, programming languages, continuous relaxation, bayesian inference, probability theory, graph neural networks, structured data, deep learning, neural networks, rnns. [OpenReview] [Google Scholar]
Taylor W. Killian, Mohamed bin Zayed University of Artificial Intelligence. Risk-sensitive reinforcement learning, offline reinforcement learning, causal inference, approximate inference, reinforcement learning, transfer learning, healthcare, bayesian neural networks. [OpenReview] [Google Scholar]
Andreas Kirsch, Google DeepMind . 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, ELLIS Institute Tübingen. Bayesian optimization hyperparameter optimization neural-architecture search automl. [OpenReview] [Google Scholar]
Adhiguna Kuncoro, DeepMind. Deep learning, natural language processing, structured prediction. [OpenReview] [Google Scholar]
Antoine Ledent, Singapore Management University. Matrix completion, statistical learning theory, interpretability regularisation, stochastic analysis. [OpenReview] [Google Scholar]
Jonathan Lee, Google. Reinforcement learning, bandits, online learning, imitation learning. [OpenReview] [Google Scholar]
Tian Li, University of Chicago. [OpenReview] [Google Scholar]
Tatiana Likhomanenko, Apple. Deep learning speech recognition nlp image recognition, deep learning face recognition image recognition video, high energy physics gradient boosting optimization model compression loss functions. [OpenReview] [Google Scholar]
Gabriel Loaiza-Ganem, Layer 6 AI. Probabilistic machine learning, deep generative models, bayesian methods, variational inference, manifold learning. [OpenReview] [Google Scholar]
Wesley J Maddox, Jump Trading. Deep learning, probabilistic models, gaussian processes, bayesian deep learning. [OpenReview]
Audra McMillan, Apple. Privacy, differential privacy, statistics. [OpenReview]
Aaron Mishkin, Computer Science Department, Stanford University. Optimization convex optimization optimization for deep learning. [OpenReview] [Google Scholar]
Mirco Mutti, Technion - Israel Institute of Technology. Reinforcement learning. [OpenReview] [Google Scholar]
Samuel Neumann, University of Alberta. Reinforcement learning, policy optimization, actor-critic. [OpenReview]
Kenta Oono, Preferred Networks, Inc.. Deep learning learning theory cnns graph neural networks. [OpenReview] [Google Scholar]
Guillermo Ortiz-Jimenez, Google DeepMind. Deep learning science, robustness, deep learning, trustworthiness. [OpenReview] [Google Scholar]
Matteo Papini, Polytechnic Institute of Milan. Reinforcement learning. [OpenReview] [Google Scholar]
Oleksandr Shchur, Amazon. Time series forecasting, temporal point processes, generative models. [OpenReview] [Google Scholar]
Jacopo Teneggi, Johns Hopkins University. Uncertainty quantification, interpretability. [OpenReview] [Google Scholar]
Austin Tripp, Valence Labs powered by recursion. Generative models, probabilistic models, gaussian process, optimization molecules, generative models molecules chemistry. [OpenReview] [Google Scholar]
Dennis Wei, International Business Machines. Explainability attribution, fairness, trustworthy, interpretability. [OpenReview] [Google Scholar]
Tijana Zrnic, Stanford University. [OpenReview]

2023 Expert Reviewers

Alexander A Alemi, Google Deepmind. Variational information bottleneck, deep learning, information theory. [OpenReview] [Google Scholar]
Yu Bai, OpenAI. Deep learning theory, uncertainty quantification, game theory, machine learning theory, reinforcement learning, large language models. [OpenReview] [Google Scholar]
Ahmad Beirami, Google. Language model alignment, language model evaluation, federated learning, natural language processing, language modeling, multi-agent systems, reinforcement learning, machine learning, information theory, statistics, signal processing. [OpenReview] [Google Scholar]
Yoshua Bengio, University of Montreal. Deep learning, ai safety, probabilistic machine learning. [OpenReview] [Google Scholar]
Alberto Bietti, Flatiron Institute. Optimization, kernel methods, deep learning theory. [OpenReview] [Google Scholar]
Pablo Samuel Castro, Google. Reinforcement learning. [OpenReview] [Google Scholar]
Zachary Charles, Google. Distributed learning communication-efficient learning federated learning. [OpenReview] [Google Scholar]
Sinho Chewi, Yale University. Optimal transportation, sampling, high-dimensional statistics. [OpenReview] [Google Scholar]
Kristy Choi, Facebook. Probabilistic models deep generative models unsupervised learning deep learning bayesian inference. [OpenReview] [Google Scholar]
Sanghyuk Chun, Department of Computer Science, Princeton University. Multi-modal learning vision and language, machine learning reliability robustness de-biasing domain generalization uncertainty estimation explainability, generative models, audio modelling music modelling, recommender systems matrix factorization collaborative filtering content-based recommender systems, multi-armed bandit online ranking algorithm, dimensionality reduction robust pca clustering. [OpenReview] [Google Scholar]
Taco Cohen, Meta. Reinforcement learning, large language models code generation, generative models deep generative models data compression, group theory representation theory equivariant networks, deep learning representation learning unsupervised learning, causality causal representation learning. [OpenReview] [Google Scholar]
Ashok Cutkosky, Boston University. Online learning stochastic optimization. [OpenReview] [Google Scholar]
Valentin De Bortoli, University of Oxford. Stochastic optimization, texture synthesis, information geometry, generative modeling, diffusion model, score-matching, schrodinger bridges. [OpenReview]
Greg Durrett, University of Texas at Austin. Question answering automated reasoning natural language inference, interpretability in nlp, text generation, text summarization, large language models pre-training. [OpenReview] [Google Scholar]
Vincent Fortuin, Technical University of Munich. Bayesian deep learning, gaussian processes, deep learning, machine learning. [OpenReview] [Google Scholar]
Tim Genewein, DeepMind. Meta-learning, ai safety, bayesian deep learning, information theory, rate-distortion theory, analysis and understanding of agents, variational inference, network compression, bounded rationality, computational neuroscience, sensorimotor learning. [OpenReview] [Google Scholar]
Steven Hansen, DeepMind. Intrinsic motivation, unsupervised reinforcement learning, deep reinforcement learning, meta reinforcement learning, model-based reinforcement learning. [OpenReview] [Google Scholar]
Manuel Haussmann, University of Southern Denmark - SDU. Bayesian statistics, bayesian deep learning, probabilistic machine learning, reinforcement learning. [OpenReview] [Google Scholar]
Matthew J. Holland, Kansai University. Learning theory. [OpenReview] [Google Scholar]
Sanghyun Hong, Oregon State University. Computer security and privacy, machine learning. [OpenReview] [Google Scholar]
Neil Houlsby, Google. Computer vision, natural language processing, machine learning. [OpenReview] [Google Scholar]
Maximilian Igl, NVIDIA. Traffic models autonomous vehicles simulation, representation learning reinforcement learning, generalization reinforcement learning, hierarchical reinforcement learning, partial observable markov decision process, reinforcement learning planning learning to plan. [OpenReview] [Google Scholar]
Andrew Ilyas, Stanford University. Adversarial examples, robustness, distribution shift. [OpenReview] [Google Scholar]
Masha Itkina, Toyota Research Institute. Robotics perception sensor fusion, deep learning computer vision convlstms video prediction recurrent models, epistemic uncertainty estimation evidential theory out-of-distribution detection, human behavior modeling occlusion inference mapping, deep generative models variational autoencoders discrete latent spaces, robot policy evaluation, failure detection and mitigation, active learning. [OpenReview] [Google Scholar]
Joonas Jälkö, University of Helsinki. Differential privacy bayesian inference variational inference. [OpenReview]
Fredrik D. Johansson, Chalmers University of Technology. Counterfactual estimation, causal inference, machine learning for healthcare, machine learning on graphs graph kernels. [OpenReview] [Google Scholar]
Daniel D. Johnson, Transluce. Uncertainty quantification, decision theory, language models, variational methods, transformers, implicit differentiation, program synthesis, programming languages, continuous relaxation, bayesian inference, probability theory, graph neural networks, structured data, deep learning, neural networks, rnns. [OpenReview] [Google Scholar]
Yannis Kalantidis, Naver Labs Europe. Self-supervised learning distillation, visual representation learning, long-tailed recognition resource-constrained deep learning video understanding, deep learning unsupervised learning representation learning vision and language, clustering nearest neighbor search image retrieval geometry indexing hashing. [OpenReview] [Google Scholar]
Khimya Khetarpal, Google. Reinforcement learning, temporal abstraction, hierarchical reinforcement learning, continual learning. [OpenReview] [Google Scholar]
Andreas Kirsch, Google DeepMind . Uncertainty quantification, active learning, information theory. [OpenReview] [Google Scholar]
Adhiguna Kuncoro, DeepMind. Deep learning, natural language processing, structured prediction. [OpenReview] [Google Scholar]
Yunwen Lei, University of Hong Kong. Stability and generalization, stochastic optimization, learning theory. [OpenReview] [Google Scholar]
Mufan Li, University of Waterloo. Deep learning theory, probability theory, pdes, stochastic analysis, learning theory, numerical analysis. [OpenReview] [Google Scholar]
Xi Lin, Xi'an Jiaotong University. Neural combinatorial optimization, multi-task learning, multi-objective optimization, bayesian optimization. [OpenReview] [Google Scholar]
Xudong Lin, Columbia University. Multimodal content understanding vision-language video analysis representation learning. [OpenReview] [Google Scholar]
Gabriel Loaiza-Ganem, Layer 6 AI. Probabilistic machine learning, deep generative models, bayesian methods, variational inference, manifold learning. [OpenReview] [Google Scholar]
Wesley J Maddox, Jump Trading. Deep learning, probabilistic models, gaussian processes, bayesian deep learning. [OpenReview]
R. Thomas McCoy, Yale University. Computational syntax, computational linguistics, inductive bias, tensor product representations, neural network interpretability. [OpenReview] [Google Scholar]
Elliot Meyerson, Cognizant AI Labs. Open-endedness creativity evolution large language models subjectivity quality diversity artificial life subjectivity multi-agent systems. [OpenReview] [Google Scholar]
Konstantin Mishchenko, Facebook. [OpenReview] [Google Scholar]
Jesse Mu, Anthropic. Emergent communication, interpretability, multi-agent communication, language and reinforcement learning, pragmatics, language grounding, deep learning. [OpenReview] [Google Scholar]
Mirco Mutti, Technion - Israel Institute of Technology. Reinforcement learning. [OpenReview] [Google Scholar]
Giannis Nikolentzos, University of Peloponnese. Graph mining, machine learning on graphs, graph kernels, graph neural networks. [OpenReview] [Google Scholar]
Atsushi Nitanda, Nanyang Technological University. Deep learning theory, mean-field optimization, kernel method, stochastic optimization. [OpenReview] [Google Scholar]
Guillermo Ortiz-Jimenez, Google DeepMind. Deep learning science, robustness, deep learning, trustworthiness. [OpenReview] [Google Scholar]
Dylan M. Paiton, University of Tuebingen. Biological vision, rnns, sparse coding, unsupervised learning, adversarial robustness. [OpenReview] [Google Scholar]
Jeff M. Phillips, University of Utah. Computational geometry, coresets and sketches, kernel density estimation, geometric data analysis, spatial scan statistics, high dimensional data. [OpenReview] [Google Scholar]
Gregory Plumb, Carnegie Mellon University. [OpenReview] [Google Scholar]
Parikshit Ram, International Business Machines. Hyperdimensional computing, compositional generalization, meta-learning, bilevel optimization, automated machine learning, large scale learning, decision trees, density estimation, clustering, nearest neighbour search. [OpenReview] [Google Scholar]
Evgenia Rusak, University of Tuebingen. [OpenReview] [Google Scholar]
Evan Shelhamer, University of British Columbia. Domain adaptation robustness test-time adaptation, few shot learning infinite mixture modeling nonparametric bayes, locality structure scale-space signal processing, fully convolutional networks, deep learning computer vision recognition. [OpenReview] [Google Scholar]
Changjian Shui, University of Ottawa. Ethics in machine learning, reliable machine learning, equity and fairness in machine learning, machine learning under distribution shift. [OpenReview] [Google Scholar]
Jake C. Snell, Princeton University. Trustworthy ml, gaussian processes, generative models, metric learning, few-shot learning. [OpenReview] [Google Scholar]
Jeremias Sulam, Johns Hopkins University. Interpretability, adversarial robustness, representation learning, sparse representations, dictionary learning. [OpenReview] [Google Scholar]
Jakub M. Tomczak, Chan Zuckerberg Initiative. Variational inference, generative ai, deep generative modeling, deep learning, derivative-free optimization, boltzmann machines, ensemble learning, svm, concept drift, change detection. [OpenReview] [Google Scholar]
Eleni Triantafillou, Google. Few-shot learning transfer learning self-supervised learning domain generalization domain adaptation, few-shot learning meta-learning, natural language processing sentence representation learning. [OpenReview] [Google Scholar]
Austin Tripp, Valence Labs powered by recursion. Generative models, probabilistic models, gaussian process, optimization molecules, generative models molecules chemistry. [OpenReview] [Google Scholar]
Russell Tsuchida, Monash University. Information geometry, point processess, deep learning, kernel methods, gaussian processes, probabilistic machine learning, implicit neural networks. [OpenReview] [Google Scholar]
Andrew Wagenmaker, University of California, Berkeley. Reinforcement learning, bandits, decision and control. [OpenReview] [Google Scholar]
Yu-Xiong Wang, School of Computer Science, Carnegie Mellon University. Few-shot learning meta-learning transfer learning, human motion prediction. [OpenReview] [Google Scholar]
Lechao Xiao, Google DeepMind. Mathematics of deep learning/ machine learning, deep learning theory, mathematics. [OpenReview] [Google Scholar]
Tianyi Zhang, Stanford University. Self-supervised learning, machine learning, natural language processing, graph neural network, language model. [OpenReview] [Google Scholar]
Tijana Zrnic, Stanford University. [OpenReview]