Algorithms for Heavy-Tailed Statistics - Regression, Covariance Estimation, and Beyond

Algorithms for Heavy-Tailed Statistics - Regression, Covariance Estimation, and Beyond

Association for Computing Machinery (ACM) via YouTube Direct link

Evidence of Hardness for Covariance Estimation

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20 of 21

Evidence of Hardness for Covariance Estimation

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Algorithms for Heavy-Tailed Statistics - Regression, Covariance Estimation, and Beyond

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

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