Internal Program Activities Breadcrumb Home Programs & Events Internal Program Activities Upcoming Past View Events by Program - All - View Events by Program No Internal Activities yet. View Events by Program - All -Quantum Research PodModern Paradigms in GeneralizationSublinear AlgorithmsError-Correcting Codes: Theory and PracticeLogic and Algorithms in Database Theory and AIData Structures and Optimization for Fast AlgorithmsAnalysis and TCS: New FrontiersSummer Cluster on Quantum ComputingMeta-ComplexityExtended Reunion: SatisfiabilityData-Driven Decision ProcessesGraph Limits and Processes on Networks: From Epidemics to MisinformationSummer Cluster: AI and HumanitySummer Cluster: Interpretable Machine LearningComputational Innovation and Data-Driven BiologySummer Cluster: Deep Learning TheoryCausalityLearning and GamesGeometric Methods in Optimization and SamplingComputational Complexity of Statistical InferenceTheoretical Foundations of Computer SystemsSatisfiability: Theory, Practice, and BeyondTheory of Reinforcement LearningProbability, Geometry, and Computation in High DimensionsLattices: Algorithms, Complexity, and CryptographyThe Quantum Wave in ComputingProofs, Consensus, and Decentralizing SocietyOnline and Matching-Based Market DesignSummer Cluster: Error-Correcting Codes and High-Dimensional ExpansionFoundations of Deep LearningData Privacy: Foundations and ApplicationsGeometry of PolynomialsFoundations of Data ScienceLower Bounds in Computational ComplexityThe Brain and ComputationReal-Time Decision MakingBridging Continuous and Discrete OptimizationFoundations of Machine LearningPseudorandomnessLogical Structures in ComputationAlgorithms and UncertaintyAlgorithmic Challenges in GenomicsCounting Complexity and Phase TransitionsFine-Grained Complexity and Algorithm DesignEconomics and ComputationCryptographyInformation TheoryAlgorithmic Spectral Graph TheoryAlgorithms and Complexity in Algebraic GeometryEvolutionary Biology and the Theory of ComputingQuantum Hamiltonian ComplexityReal Analysis in Computer ScienceTheoretical Foundations of Big Data Analysis View Events by Program Fellows Talk - Tselil Schramm & Zhuoran Yang Fellows Talk - Tselil Schramm & Zhuoran Yang Probability, Geometry, and Computation in High Dimensions Tselil Schramm (Stanford University); Zhuoran Yang (Princeton University) 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 On (Non-Traditional) Costs and Potentials On (Non-Traditional) Costs and Potentials Probability, Geometry, and Computation in High Dimensions Shiri Artstein (Tel-Aviv University) Reading Group: Causality and Econometrics Reading Group: Causality and Econometrics Theory of Reinforcement Learning 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 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 Pagination First page First Previous page Previous Page 80 Page 81 Current page 82 Page 83 Page 84 Next page Next Last page Last
