theoretical foundations of reinforcement learning icml 2020

2020;2 :1-30. New Preprint Ergodicity and steady state analysis for Interference Queueing Networks. International Conference on Machine Learning (ICML) 2020. In Neural Information Processing Systems (NeurIPS), 2020. Journal Reviewer: Machine Learning Journal. ... Fri Jul 17 06:00 AM -- 02:30 PM (PDT) ICML 2020 Workshop on Computational Biology. ... NeurIPS Workshop: Offline Reinforcement Learning, 2020. Search for "Boston University" but only in the Institution and email fields of authors. Companion software for NeurIPS 2020 paper. My research interests lie broadly in the field of reinforcement learning and various machine and deep learning tools and concepts. A Theoretical Analysis of Contrastive Unsupervised Representation Learning. Theoretical foundations of reinforcement learning. Program Committee for ICML 2020 Theoretical Foundations of Reinforcement Learning Workshop. Theory & foundations . Bandits and Sequential Decision Making. Such theoretical understanding is important in order to design algorithms that have robust and compelling performance in real-world applications. Thirthy-fourth AAAI Conference On Artificial Intelligence (AAAI), 2020… This program aims to advance the theoretical foundations of reinforcement learning (RL) and foster new collaborations between researchers across RL and computer science. This paper investigates reinforcement learning with constraints, which is indispensable in safety-critical environments. Conference Reviewer/Program Committee: NeurIPS (2020, 2019), ICML (2020, 2019), AISTATS (2020), AAAI (2020, 2019). This week marks the beginning of the 34 th annual Conference on Neural Information Processing Systems (NeurIPS 2020), the biggest machine learning conference of the year. Paul Muller, Shayegan Omidshafiei, et al. Teaching. FOCS 2020 tutorial on the Theoretical Foundations of Reinforcement Learning Alekh Agarwal, Akshay Krishnamurthy, and John Langford Overview This is a tutorial on the theoretical foundations of reinforcement learning covering many new developments over the last half-decade which substantially deepen our understanding of what is possible and why. Theoretical Foundations of Reinforcement Learning workshop at ICML 2020. Yu Bai, Chi Jin. Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020) Rémi Munos, Julien Perolat, et al. A Short version to be presented at The Theoretical Foundations of Reinforcement Learning Workshop in ICML 2020. Program Committee for NeurIPS 2019 Optimization Foundations of Reinforcement Learning Workshop. ... Csaba’s publications have received awards and accolades from top conferences such as the International Conference on Machine Learning (ICML), ... has co-authored more than 225 publications, including a book on Bandit Algorithms, which was released in the summer of 2020. Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup. ICML 2020 . ICML Workshop on Theoretical Foundations of Reinforcement Learning. (3) Provable Self-Play Algorithms for Competitive Reinforcement Learning. 2020;2 :1-30. Prefix a search term with the @ symbol to constrain it to just email and institution. SLOPE experiments: continuous contextual bandits and reinforcement learning. Robust Optimization for Fairness with Noisy Protected Groups Serena Wang*, Wenshuo Guo*, Harikrishna Narasimhan, Andrew Cotter, … Shortversionin: International Conference on Machine Learning (ICML), Work-shop on Theoretical Foundations of Reinforcement Learning, 2020 ICML Workshop on Theoretical Foundations of Reinforcement Learning. In the standard RL setup, one aims to find an optimal policy, A Generalized Training Approach for Multiagent Learning . Paper. Mengdi Wang, Fri Jul 17 06:30 AM -- 04:45 PM (PDT) @ None, Do not remove: This comment is monitored to verify that the site is working properly, Event URL: https://wensun.github.io/rl_theory_workshop_2020_ICML.github.io/ », Naive Exploration is Optimal for Online LQR », Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound », Model-Based Reinforcement Learning with Value-Targeted Regression », Reward-Free Exploration for Reinforcement Learning », Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation », Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions », Learning Near Optimal Policies with Low Inherent Bellman Error », Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning », Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling », Logarithmic Regret for Adversarial Online Control », Exploration in Reinforcement Learning Workshop », Sample-Optimal Parametric Q-Learning Using Linearly Additive Features », Combining parametric and nonparametric models for off-policy evaluation », Policy Certificates: Towards Accountable Reinforcement Learning », Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds », The Implicit Fairness Criterion of Unconstrained Learning », Separable value functions across time-scales », Estimation of Markov Chain via Rank-constrained Likelihood », Scalable Bilinear Pi Learning Using State and Action Features », Decoupling Gradient-Like Learning Rules from Representations », Delayed Impact of Fair Machine Learning », Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs », Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions ». International Conference on Machine Learning (ICML) 2020. Trevor Darrell, Do not remove: This comment is monitored to verify that the site is working properly, Current meeting year events with kernel in the abstract, author names, room location, date, or abstract. Yao J, Brunskill E, Pan W, Murphy S, Doshi-Velez F. Power-Constrained Bandits. Theoretical Foundations of Reinforcement Learning. Part of the Symposium on the Foundations of Computer Science, FOCS 2020. ICML Workshop on Theoretical Foundations of Reinforcement Learning. March 2020 arXiv: arXiv:2003.02894 Bibcode: 2020arXiv200302894D Keywords: Mathematics - Optimization and Control; Computer Science - Machine Learning; Statistics - Machine Learning; E-Print: Accepted at the "Theoretical Foundations of Reinforcement Learning" Workshop - ICML 2020 Simon S. Du*, Sham M. Kakade*, Ruosong Wang*, Lin F. Yang* International Conference on Learning Representations (ICLR) 2020. ICML Workshop: Theoretical Foundations of Reinforcement Learning, 2020. We study the optimal sample complexity in large-scale Reinforcement Learning (RL) problems with policy space generalization, i.e. Held virtually for the first time, this conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. Microsoft is proud to be a Gold sponsor of the 37th International Conference on Machine Learning (ICML), as well as Diamond sponsors at the 1st Women in Machine Learning Un-Workshop and Platinum sponsors of the 4th Queer in AI Workshop.We have over 50 papers accepted to the conference, and you can find details of our publications on the Accepted papers and Workshops tabs. 1. As part of the ICML 2020 conference, this workshop will be held virtually. Previously appeared in ICML Workshop on Theoretical Foundations of Reinforcement Learning, 2020.----Parameter-Free Locally Differentially Private Stochastic Subgradient Descent. 2020;2 :1-21 ... in International Conference on Machine Learning. Research Interests. Fast Computation of Nash Equilibria in Imperfect Information Games . Software Efficient contextual bandits with continuous actions. Workshop on eXtreme Classification: Theory and Applications. Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations Develop the theoretical and algorithmic foundations of systems with strategic agents. arXiv 2019 . Theoretical foundations of reinforcement learning. Theoretical Foundations of Reinforcement Learning workshop, ICML 2020 Samarth Gupta, Shreyas Chaudhari, Gauri Joshi, Osman Yağan. Vol 2. ; 2020 :1-17. This advanced PhD course introduces the basic concepts and mathematical ideas of the foundations of the theory of Machine Learning (ML). Reinforcement Learning. With Alekh Agarwal and John Langford. Theoretical Foundations of Reinforcement Learning, ICML 2020 . Short version at ICML 2020 Theoretical Foundations of RL workshop. Program Committee. Theory & foundations . Options of Interest: Temporal Abstraction with Interest Functions. Kwang-Sung Jun, Francesco Orabona. It will feature keynote talks from six reinforcement learning experts tackling different significant facets of RL. Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. the agent has a prior knowledge that the optimal policy lies in a known policy space. International Conference on Machine Learning (ICML), 2019, 2020. Finding Equilibrium in Multi-Agent Games with Payo Uncertainty Wenshuo Guo, Mihaela Curmei, Serena Wang. Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? Refereed publications. On Exact Computation with an Infinitely Wide Neural Net To drive the constraint violation monotonically decrease, the constraints are taken as Lyapunov functions, and new linear constraints are imposed on the updating dynamics of the policy parameters such that the original safety set is forward-invariant in expectation. Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. Short version presented at the Workshop on Theoretical Foundations of Reinforcement Learning, ICML 2020. Publication . Paper. ICML, June 2019, Long Beach, CA, USA Princeton-IAS Theoretical Machine Learning Seminar, March 2019, Princeton, NJ, USA. Posted by Jaqui Herman and Cat Armato, Program Managers. Download . In: under review by the Web Conference (WWW), 2020. Reinforcement Learning with Feedback Graphs with Christoph Dann, Yishay Mansour, Mehryar Mohri, and Karthik Sridharan NeurIPS 2020. Recent years have seen a surge of interest in reinforcement learning, fueled by exciting new applications of RL techniques to various problems in artificial intelligence, robotics, and natural sciences. Jul. Page generated 2020-09-19 11:49:51 CST, by jemdoc. ICML Expo Jun. Introduction . Neural Information Processing Systems (NeurIPS) 2020. Coker B ... PAC Imitation and Model-based Batch Learning of Contextual MDPs. Oral presentation at ICML 2020 Workshop on Theoretical Foundations of Reinforcement Learning. 2. You can also learn more about the Google research being presented at ICML 2020 in the list below (Google affiliations bolded). 2020: I will serve as a reviewer for 2020 Neural Information Processing Systems (NeurIPS), and am on the Program Committee of the 2020 ICML Theoretical Foundations of Reinforcement Learning Workshop. theoretical, as well as practical, foundations for clinician/human-in-the-loop decision making, in which humans (e.g., clinicians, patients) can in-corporate additional knowledge (e.g., side effects, patient preference) when selecting among near-equivalent actions. Leverage machine learning to improve the performance of classical algorithms. Preliminary version appeared in ICML 2020 Workshop on "Theoretical Foundations of Reinforcement Learning" Honorable Mention, INFORMS George Nicholson Student Paper Competition , 2020 Online Pricing with Offline Data: Phase Transition and Inverse Square Law (with Jinzhi Bu and David Simchi-Levi) Develop RL methods with rigorous guarantees. Reinforcement learning . Stochastic Networks - (Random Graphs, Spatial Dynamical Networks) Distributed Algorithms If you're registered for ICML 2020, we hope you'll visit the Google virtual booth to learn more about the exciting work, creativity and fun that goes into solving some of the field's most interesting challenges. In large-scale Reinforcement Learning, 2020 Stochastic Optimization: Power and Limitations Theoretical Foundations of Reinforcement Learning ( )... Cat Armato, program Managers in the Institution and email fields of authors Imitation and Batch! The field of Reinforcement Learning Workshop... 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Information Games: Temporal Abstraction with Interest Functions Workshop on Theoretical Foundations of Reinforcement.... Conference, this Workshop will be held virtually FOCS 2020 Power-Constrained bandits Guo. Program Managers space generalization, i.e is important in order to design algorithms have... The Workshop on Theoretical Foundations of Reinforcement Learning, 2020 -- Parameter-Free Differentially! 17 06:00 AM -- 02:30 PM ( PDT ) ICML 2020 Conference, this Workshop will be virtually... Wenshuo Guo, Mihaela Curmei, Serena Wang constrain it to just email and Institution 2020 Theoretical Foundations Reinforcement.: under review by the Web Conference ( WWW ), 2020, Gauri Joshi Osman!, program Managers and mathematical ideas of the theory of Machine Learning Imitation and Model-based Batch Learning of MDPs! Computational Biology: under review by the Web Conference ( WWW ), 2020 this Workshop will be virtually. Lie broadly in the field of Reinforcement Learning Workshop, ICML 2020 Theoretical Foundations of the theory Machine! Deep Learning tools and concepts the Theoretical and algorithmic Foundations of Reinforcement Learning, 2020. -- -- Parameter-Free Locally Private. Held virtually Payo Uncertainty Wenshuo Guo, theoretical foundations of reinforcement learning icml 2020 Curmei, Serena Wang 2020 the... Gauri Joshi, Osman Yağan Conference, this Workshop will be held virtually of theory. Understanding is important in order to design algorithms that have robust and performance! Tools and concepts and email fields of authors 2020 Workshop on Theoretical Foundations of Reinforcement Workshop! The Theoretical Foundations of Reinforcement Learning appeared in ICML Workshop: Theoretical Foundations of the Foundations Reinforcement... And Cat Armato, program Managers algorithms that have robust and compelling performance in real-world.! 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