Computationally Tractable and Near Optimal Design of Experiments

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=PFNHHQ6vOHA


Duration: 1:04:42
2,434 views
14

Aarti Singh, Carnegie Mellon University
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/aarti-singh-2017-5-5



Other Videos By Simons Institute for the Theory of Computing


2017-08-22Robustness, Stochastics, Uncertainty 2
2017-08-22Robustness, Stochastics, Uncertainty 1
2017-08-22Continuous Methods 2
2017-08-22Continuous Methods 1
2017-08-22Duality 3
2017-08-21LP/SDP Hierarchies and Sum of Squares Proofs 2
2017-08-21Duality 2
2017-08-21Duality 1
2017-05-05Learning from Untrusted Data
2017-05-05The “Tell Me Something New” Model of Computation for Machine Learning
2017-05-05Computationally Tractable and Near Optimal Design of Experiments
2017-05-05Computational Challenges and the Future of ML Panel
2017-05-04The Polytope Learning Problem
2017-05-04System and Algorithm Co-Design, Theory and Practice, for Distributed Machine Learning
2017-05-04Does Computational Complexity Restrict Artificial Intelligence (AI) and Machine Learning?
2017-05-04Computational Efficiency and Robust Statistics
2017-05-04Robust Estimation of Mean and Covariance
2017-05-04Embedding as a Tool for Algorithm Design
2017-05-03The Imitation Learning View of Structured Prediction
2017-05-03Exponential Computational Improvement by Reduction
2017-05-03Machine Learning Combinatorial Optimization Algorithms


Tags:
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Computational Challenges in Machine Learning
Aarti Singh