Near-Optimal Learning of Tree-Structured Distributions by Chow-Liu

Channel:

Subscribers:

68,700

Published on December 18, 2020 7:40:40 AM ● Video Link: https://www.youtube.com/watch?v=Qg5HDA9abH8

Duration: 52:36

1,002 views

Eric Price (University of Texas, Austin)
https://simons.berkeley.edu/talks/tbd-258
Learning and Testing in High Dimensions

Other Videos By Simons Institute for the Theory of Computing

2021-01-08	Pseudorandomness
2021-01-08	Crypto and Privacy
2021-01-08	Matchings
2021-01-08	Introductory Remarks
2020-12-28	David Harold Blackwell Summer Research Institute \| Interview with Jelani Nelson
2020-12-19	Recent Developments in Supervised Learning With Noise
2020-12-19	Testing and Reconstruction via Decision Trees
2020-12-18	An Equivalence Between Private Classification and Online Prediction
2020-12-18	Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models
2020-12-18	Hardness of Identity Testing for Potts models and RBMs
2020-12-18	Near-Optimal Learning of Tree-Structured Distributions by Chow-Liu
2020-12-17	Learning Some Ill-Conditioned Gaussian Graphical Models
2020-12-17	Directed Graphical Models for Extreme Value Statistics
2020-12-17	Learning Deep ReLU Networks is Fixed-Parameter Tractable
2020-12-17	The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks in High Dimension
2020-12-16	Analyzing Optimization and Generalization in Deep Learning via Dynamics of Gradient Descent
2020-12-16	SGD Learns One-Layer Networks in WGANs
2020-12-16	Recent Progress in Algorithmic Robust Statistics via the Sum-of-Squares Method
2020-12-16	Phase Transitions for Detecting Latent Geometry in Random Graphs
2020-12-16	Low-Degree Hardness of Random Optimization Problems
2020-12-15	Counting and Sampling Subgraphs in Sublinear Time

Tags:

Simons Institute

theoretical computer science

UC Berkeley

Computer Science

Theory of Computation

Theory of Computing

Learning and Testing in High Dimensions

Eric Price