Mixing Implies Lower Bounds for Space Bounded Learning

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


Duration: 49:46
775 views
5

Dana Moshkovitz, University of Texas, Austin
https://simons.berkeley.edu/talks/dana-moshkovitz-2017-03-08
Proving and Using Pseudorandomness



Other Videos By Simons Institute for the Theory of Computing


2017-03-10A Reduction from Efficient Non-Malleable Extractors to Low-Error Two-Source Extractors
2017-03-10Randomness Intuition
2017-03-10Fast Permutation Property Testing and Metrics of Permutations
2017-03-09Query-to-Communication Lifting for BPP
2017-03-09Pseudorandom Generators from Pseudorandom Multi-Switching Lemmas
2017-03-09PRGs for Small Space via Fourier Analysis
2017-03-09Deterministic Isolation for Bipartite Matching and Matroid Intersection
2017-03-09Derandomizing "Algebraic RL"
2017-03-08Linear-Algebraic Pseudorandomness: Subspace Designs and Dimension Expanders
2017-03-08Algorithmic Regularity Lemmas and Applications
2017-03-08Mixing Implies Lower Bounds for Space Bounded Learning
2017-03-08Preserving Randomness for Adaptive Adversaries
2017-03-07Explicit, Almost Optimal, Epsilon-Balanced Codes
2017-03-07Forcing Quasirandomness with Triangles
2017-03-07Removal Lemmas with Polynomial Bounds
2017-03-06The Number of B_h-sets of a Given Cardinality
2017-03-06Regularity Inheritance in Pseudorandom Graphs
2017-03-06Packing Degenerate Graphs Using Pseudorandomness
2017-03-06Pseudorandomness in Data Structures
2017-03-06Approximately Counting Solutions to Systems of Quadratic Equations
2017-03-06The Uncanny Usefulness of Constructive Proofs of Pseudorandomness


Tags:
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Proving and Using Pseudorandomness
Dana Moshkovitz