Search for "Boston University" but only in the Institution and email fields of authors. Thirthy-fourth AAAI Conference On Artificial Intelligence (AAAI), 2020… You can also learn more about the Google research being presented at ICML 2020 in the list below (Google affiliations bolded). Develop RL methods with rigorous guarantees. In: under review by the Web Conference (WWW), 2020. Posted by Jaqui Herman and Cat Armato, Program Managers. Fast Computation of Nash Equilibria in Imperfect Information Games . 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. International Conference on Machine Learning (ICML) 2020. With Alekh Agarwal and John Langford. Theoretical foundations of reinforcement learning. ICML Workshop on Theoretical Foundations of Reinforcement Learning. Oral presentation at ICML 2020 Workshop on Theoretical Foundations of Reinforcement Learning. the agent has a prior knowledge that the optimal policy lies in a known policy space. 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. ICML 2020 . Yu Bai, Chi Jin. Simon S. Du*, Sham M. Kakade*, Ruosong Wang*, Lin F. Yang* International Conference on Learning Representations (ICLR) 2020. 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. This program aims to advance the theoretical foundations of reinforcement learning (RL) and foster new collaborations between researchers across RL and computer science. SLOPE experiments: continuous contextual bandits and 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 arXiv 2019 . Leverage machine learning to improve the performance of classical algorithms. Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. It will feature keynote talks from six reinforcement learning experts tackling different significant facets of RL. 2020;2 :1-30. This paper investigates reinforcement learning with constraints, which is indispensable in safety-critical environments. ICML Workshop on Theoretical Foundations of Reinforcement Learning. Conference Reviewer/Program Committee: NeurIPS (2020, 2019), ICML (2020, 2019), AISTATS (2020), AAAI (2020, 2019). ICML Expo As part of the ICML 2020 conference, this workshop will be held virtually. Shortversionin: International Conference on Machine Learning (ICML), Work-shop on Theoretical Foundations of Reinforcement Learning, 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. Publication . In the standard RL setup, one aims to find an optimal policy, Part of the Symposium on the Foundations of Computer Science, FOCS 2020. A Generalized Training Approach for Multiagent Learning . Kwang-Sung Jun, Francesco Orabona. Short version at ICML 2020 Theoretical Foundations of RL workshop. Robust Optimization for Fairness with Noisy Protected Groups Serena Wang*, Wenshuo Guo*, Harikrishna Narasimhan, Andrew Cotter, … 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. Download . ICML, June 2019, Long Beach, CA, USA Princeton-IAS Theoretical Machine Learning Seminar, March 2019, Princeton, NJ, USA. Journal Reviewer: Machine Learning Journal. We study the optimal sample complexity in large-scale Reinforcement Learning (RL) problems with policy space generalization, i.e. Theoretical Foundations of Reinforcement Learning workshop at ICML 2020. Options of Interest: Temporal Abstraction with Interest Functions. Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020) Stochastic Networks - (Random Graphs, Spatial Dynamical Networks) Distributed Algorithms 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. Theory & foundations . Theoretical Foundations of Reinforcement Learning, ICML 2020 . 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. Program Committee for NeurIPS 2019 Optimization Foundations of Reinforcement Learning Workshop. Page generated 2020-09-19 11:49:51 CST, by jemdoc. Paper. Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? In Neural Information Processing Systems (NeurIPS), 2020. International Conference on Machine Learning (ICML), 2019, 2020. Jun. 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. Software Efficient contextual bandits with continuous actions. Bandits and Sequential Decision Making. Coker B ... PAC Imitation and Model-based Batch Learning of Contextual MDPs. Reinforcement learning . Vol 2. ; 2020 :1-17. Refereed publications. Teaching. 1. 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) ... 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. Theoretical Foundations of Reinforcement Learning workshop, ICML 2020 Samarth Gupta, Shreyas Chaudhari, Gauri Joshi, Osman Yağan. Research Interests. Program Committee for ICML 2020 Theoretical Foundations of Reinforcement Learning Workshop. Introduction . A Short version to be presented at The Theoretical Foundations of Reinforcement Learning Workshop in ICML 2020. On Exact Computation with an Infinitely Wide Neural Net Previously appeared in ICML Workshop on Theoretical Foundations of Reinforcement Learning, 2020.----Parameter-Free Locally Differentially Private Stochastic Subgradient Descent. ... Fri Jul 17 06:00 AM -- 02:30 PM (PDT) ICML 2020 Workshop on Computational Biology. International Conference on Machine Learning (ICML) 2020. Develop the theoretical and algorithmic foundations of systems with strategic agents. Short version presented at the Workshop on Theoretical Foundations of Reinforcement Learning, ICML 2020. Paper. Reinforcement Learning. This advanced PhD course introduces the basic concepts and mathematical ideas of the foundations of the theory of Machine Learning (ML). Theoretical Foundations of Reinforcement Learning. 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, 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. Theory & foundations . A Theoretical Analysis of Contrastive Unsupervised Representation Learning. Paul Muller, Shayegan Omidshafiei, et al. Such theoretical understanding is important in order to design algorithms that have robust and compelling performance in real-world applications. ICML Workshop: Theoretical Foundations of Reinforcement Learning, 2020. Prefix a search term with the @ symbol to constrain it to just email and institution. Workshop on eXtreme Classification: Theory and Applications. Rémi Munos, Julien Perolat, et al. (3) Provable Self-Play Algorithms for Competitive Reinforcement Learning. Held virtually for the first time, this conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations Neural Information Processing Systems (NeurIPS) 2020. Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup. 2020;2 :1-21 ... in International Conference on Machine Learning. 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 ». Finding Equilibrium in Multi-Agent Games with Payo Uncertainty Wenshuo Guo, Mihaela Curmei, Serena Wang. New Preprint Ergodicity and steady state analysis for Interference Queueing Networks. Yao J, Brunskill E, Pan W, Murphy S, Doshi-Velez F. Power-Constrained Bandits. Theoretical foundations of reinforcement learning. Program Committee. Companion software for NeurIPS 2020 paper. 2020;2 :1-30. Jul. 2. ... NeurIPS Workshop: Offline Reinforcement Learning, 2020. ICML Workshop on Theoretical Foundations of Reinforcement Learning. My research interests lie broadly in the field of reinforcement learning and various machine and deep learning tools and concepts. Reinforcement Learning with Feedback Graphs with Christoph Dann, Yishay Mansour, Mehryar Mohri, and Karthik Sridharan NeurIPS 2020. Is indispensable in safety-critical environments develop the Theoretical Foundations of Reinforcement Learning and various Machine deep. 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