Discussion: Structure and RL

Discussion: Structure and RL

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=I-5E4eDE9sI


Duration: 59:15
1,207 views
15

Theory of Reinforcement Learning Reunion



Other Videos By Simons Institute for the Theory of Computing


2021-12-01A Productization Property/Trick For H-Stable(And Hopefully Strongly Log-Concave) Polynomials
2021-12-01Panel on Making Predictions in a Quantum World | Quantum Colloquium
2021-12-01Making Predictions in a Quantum World | Quantum Colloquium
2021-12-01Sampling Matrices From Harish-Chandra-Itzykson-Zuber Densities
2021-11-30Continuous Maximum Entropy Distributions
2021-11-30Foundations of Computing: Tal Rabin | Polylogues
2021-11-30Constrained Optimization On Riemannian Manifolds
2021-11-30Near Optimal Sample Complexity For Matrix And Tensor Normal Models Via Geodesic Convexity
2021-11-30Computing The Nc-Rank Via Discrete Convex Optimization On Cat(0) Spaces
2021-11-29Norm Minimization, Invariant Theory, And Quantum Tomography
2021-11-19Discussion: Structure and RL
2021-11-17Panel on Verifiable Quantum Advantage | Quantum Colloquium
2021-11-17Interactive Protocols for Classically-Verifiable Quantum Advantage with an Ion-Trap Quantum Computer
2021-11-11On the Cryptographic Hardness of Learning Single Periodic Neurons
2021-11-11Worst-Case Robustness in Machine Learning
2021-11-11Computational trade-offs in graph models
2021-11-11Sum-Of-Squares Lower Bound for Statistical Problems
2021-11-10Analyzing Average-Case Complexity by Meta-Complexity
2021-11-10Panel on Achieving Quantum Advantage in the NISQ Era | Quantum Colloquium
2021-11-09On The Hardness Of Average-Case k-SUM
2021-11-09Low-Complexity Cryptography and Simple Hard-to-Learn Functions


Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Theory of Reinforcement Learning Reunion