Submodularity: Theory and Applications I

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=Y3u_hvxayDY


Duration: 1:04:01
16,008 views
268

Stefanie Jegelka, MIT
https://simons.berkeley.edu/talks/andreas-krause-stefanie-jegelka-01-23-2017-1
Foundations of Machine Learning Boot Camp



Other Videos By Simons Institute for the Theory of Computing


2017-01-26Tutorial on Deep Learning I
2017-01-25Nonparametric Bayesian Methods: Models, Algorithms, and Applications IV
2017-01-25Nonparametric Bayesian Methods: Models, Algorithms, and Applications II
2017-01-25Nonparametric Bayesian Methods: Models, Algorithms, and Applications III
2017-01-25Nonparametric Bayesian Methods: Models, Algorithms, and Applications I
2017-01-24Deep Robotic Learning
2017-01-24Interactive Learning of Classifiers and Other Structures
2017-01-24A Tutorial on Reinforcement Learning II
2017-01-24A Tutorial on Reinforcement Learning I
2017-01-23Submodularity: Theory and Applications II
2017-01-23Submodularity: Theory and Applications I
2017-01-23Optimization for Machine Learning II
2017-01-23Optimization for Machine Learning I
2017-01-20Pseudorandom Generators IV
2017-01-20Pseudorandomness and Regularity in Graphs IV
2017-01-20Arithmetic Applications of Pseudorandomness IV
2017-01-20Pseudorandom Generators III
2017-01-20Extractors and Expanders IV
2017-01-19Pseudorandom Generators II
2017-01-19Extractors and Expanders III
2017-01-19Pseudorandom Generators I


Tags:
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Foundations of Machine Learning Boot Camp
Stefanie Jegelka