Internal Program Activities Breadcrumb Home Programs & Events Internal Activities Upcoming Past View Events by Program - All - View Events by Program No Internal Activities yet. View Events by Program - All -Probability, Geometry, and Computation in High DimensionsTheory of Reinforcement Learning View Events by Program Robustly Learning Mixtures of (Clusterable) Gaussians via the SoS Proofs to Algorithms Method Robustly Learning Mixtures of (Clusterable) Gaussians via the SoS Proofs to Algorithms Method Probability, Geometry, and Computation in High Dimensions Sam Hopkins, UC Berkeley Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning Theory of Reinforcement Learning Yu-Xiang Wang (UC Santa Barbara) On Distance Approximation for Graph Properties On Distance Approximation for Graph Properties Probability, Geometry, and Computation in High Dimensions Dana Ron, Tel-Aviv University Reading Group: Causality and Econometrics Reading Group: Causality and Econometrics Theory of Reinforcement Learning Fellows Talk - Ashwin Pananjady Fellows Talk - Ashwin Pananjady Probability, Geometry, and Computation in High Dimensions Ashwin Pananjady (Georgia Institute of Technology) Open Problem Discussion Open Problem Discussion Theory of Reinforcement Learning Reading Group: Deep RL and Function Approximation Reading Group: Deep RL and Function Approximation Theory of Reinforcement Learning RL Seminar: Provably Efficient Exploration in Policy Optimization RL Seminar: Provably Efficient Exploration in Policy Optimization Theory of Reinforcement Learning Zhuoran Yang (Princeton) Open Problem Discussion Open Problem Discussion Theory of Reinforcement Learning Reading Group: Causality and Econometrics Reading Group: Causality and Econometrics Theory of Reinforcement Learning Pagination First page First Previous page Previous Page 83 Page 84 Current page 85 Page 86 Page 87 Next page Next Last page Last
