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Intro
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Classroom Contents
Algorithms for Heavy-Tailed Statistics - Regression, Covariance Estimation, and Beyond
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- 1 Intro
- 2 High Probability Estimation
- 3 Gaussian Covariance Estimation
- 4 Gaussian Linear Regression
- 5 Covariance Estimation under Weak Assumptions
- 6 Linear Regression Rates under Weak Assumptions
- 7 Key SOS Assumptions
- 8 Towards Statistical Optimality for Covariance Estimation
- 9 Towards Statistical Optimality for Linear Regression
- 10 Outline
- 11 Median of Means Framework
- 12 Median of Means - One Dimensional Case
- 13 Tournament Estimator - High Dimensional Version
- 14 Testing a Candidate Matrix - Optimization Problem
- 15 Sos Relaxation - Analysis
- 16 Sos Relaxation - Concentration Step
- 17 Sos Relaxation - Expectation Step
- 18 Matrix Bernstein?
- 19 Getting Around Matrix Bernstein
- 20 Evidence of Hardness for Covariance Estimation
- 21 Low degree Tests for Detection
