Program Committee for ICML 2020 Theoretical Foundations of Reinforcement Learning Workshop. Paper. Prefix a search term with the @ symbol to constrain it to just email and institution. International Conference on Machine Learning (ICML) 2020. On Exact Computation with an Infinitely Wide Neural Net Held virtually for the first time, this conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. Part of the Symposium on the Foundations of Computer Science, FOCS 2020. Theory & foundations . ... 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. Introduction . Search for "Boston University" but only in the Institution and email fields of authors. Conference Reviewer/Program Committee: NeurIPS (2020, 2019), ICML (2020, 2019), AISTATS (2020), AAAI (2020, 2019). ICML Workshop on Theoretical Foundations of Reinforcement Learning. 2. 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. Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? It will feature keynote talks from six reinforcement learning experts tackling different significant facets of RL. Shortversionin: International Conference on Machine Learning (ICML), Work-shop on Theoretical Foundations of Reinforcement Learning, 2020 Finding Equilibrium in Multi-Agent Games with Payo Uncertainty Wenshuo Guo, Mihaela Curmei, Serena Wang. Paper. arXiv 2019 . Stochastic Networks - (Random Graphs, Spatial Dynamical Networks) Distributed Algorithms International Conference on Machine Learning (ICML) 2020. Leverage machine learning to improve the performance of classical algorithms. Theoretical Foundations of Reinforcement Learning, ICML 2020 . Refereed publications. A Theoretical Analysis of Contrastive Unsupervised Representation Learning. Teaching. Bandits and Sequential Decision Making. This program aims to advance the theoretical foundations of reinforcement learning (RL) and foster new collaborations between researchers across RL and computer science. Robust Optimization for Fairness with Noisy Protected Groups Serena Wang*, Wenshuo Guo*, Harikrishna Narasimhan, Andrew Cotter, … 2020;2 :1-30. 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. Posted by Jaqui Herman and Cat Armato, Program Managers. In the standard RL setup, one aims to ﬁnd an optimal policy, Thirthy-fourth AAAI Conference On Artificial Intelligence (AAAI), 2020… Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations ... Fri Jul 17 06:00 AM -- 02:30 PM (PDT) ICML 2020 Workshop on Computational Biology. Publication . ICML Workshop: Theoretical Foundations of Reinforcement Learning, 2020. Workshop on eXtreme Classification: Theory and Applications. Theoretical foundations of reinforcement learning. Download . 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. New Preprint Ergodicity and steady state analysis for Interference Queueing Networks. Yu Bai, Chi Jin. Such theoretical understanding is important in order to design algorithms that have robust and compelling performance in real-world applications. Previously appeared in ICML Workshop on Theoretical Foundations of Reinforcement Learning, 2020.----Parameter-Free Locally Differentially Private Stochastic Subgradient Descent. We study the optimal sample complexity in large-scale Reinforcement Learning (RL) problems with policy space generalization, i.e. 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 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. With Alekh Agarwal and John Langford. Theory & foundations . Coker B ... PAC Imitation and Model-based Batch Learning of Contextual MDPs. Kwang-Sung Jun, Francesco Orabona. This advanced PhD course introduces the basic concepts and mathematical ideas of the foundations of the theory of Machine Learning (ML). ICML Workshop on Theoretical Foundations of Reinforcement Learning. 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. Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020) 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. Page generated 2020-09-19 11:49:51 CST, by jemdoc. Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup. Short version at ICML 2020 Theoretical Foundations of RL workshop. 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 ». Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. Companion software for NeurIPS 2020 paper. As part of the ICML 2020 conference, this workshop will be held virtually. My research interests lie broadly in the field of reinforcement learning and various machine and deep learning tools and concepts. 2020;2 :1-30. SLOPE experiments: continuous contextual bandits and reinforcement learning. A Short version to be presented at The Theoretical Foundations of Reinforcement Learning Workshop in ICML 2020. 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. In Neural Information Processing Systems (NeurIPS), 2020. Neural Information Processing Systems (NeurIPS) 2020. Research Interests. Journal Reviewer: Machine Learning Journal. ICML, June 2019, Long Beach, CA, USA Princeton-IAS Theoretical Machine Learning Seminar, March 2019, Princeton, NJ, USA. 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. ICML Workshop on Theoretical Foundations of Reinforcement Learning. ICML 2020 . Jun. Program Committee. Simon S. Du*, Sham M. Kakade*, Ruosong Wang*, Lin F. Yang* International Conference on Learning Representations (ICLR) 2020. Theoretical Foundations of Reinforcement Learning workshop at ICML 2020. 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