Все публикации

0:05:57

What is so amazing about Wigner's semicircle law?

0:19:47

The curse and the blessing of high dimensions

1:10:11

RM+ML: 29. Free Probability Theory and Linearization of Non-Linear Problems

0:53:13

RM+ML: 28. Properties of the Neural Tangent Kernel

1:25:57

RM+ML: 27. Time Evolution of Learning and Neural Tangent Kernel

1:13:12

RM+ML: 26. Gradient Descent for Linear Regression

0:16:34

RM+ML: 25. Gaussian Equivalence Principle for Non-Linear Random Features

1:36:12

RM+ML: 24. Calculation of the Random Feature Eigenvalue Distribution

1:19:34

RM+ML: 23. Cumulants and Their Properties and Uses

0:57:40

RM+ML: 21. Another Proof of Marchenko-Pastur -- Via Stein's Identity

0:53:38

RM+ML: 18. Double Descent and Linear Regression: Under-Determined Case

1:00:54

RM+ML: 17. Double Descent and Linear Regression: Over-Determined Case

0:22:59

RM+ML: 16. Proof of the Signal-Plus-Noise Theorem

0:54:21

RM+ML: 15. Spiked Signal-Plus-Noise Model

0:31:49

RM+ML: 14. Proof of Marchenko-Pastur: Stieltjes Inversion Formula

0:24:31

RM+ML: 13. Proof of Marchenko-Pastur: Equation for Stieltjes Transform

1:07:18

RM+ML: 12. Preparations for Proof of Marchenko-Pastur Law

0:59:39

RM+ML: 11. The Marchenko-Pastur Law for Wishart Matrices

1:05:03

RM+ML: 9. Wishart Random Matrices and Concentration of Largest Eigenvalue

0:26:26

RM+ML: 10. Proof of Concentration of Largest Eigenvalue

0:43:24

RM+ML: 8. General Remarks on Linear and Non-Linear Concentration Inequalities

1:05:20

RM+ML: 7. Proof of Non-Linear Concentration for Gaussian Random Vectors

1:30:06

RM+ML: 6. Non-Linear Concentration of Gaussian Random Vectors for Lipschitz Functions.

1:23:07

RM+ML: 5. Exponential Concentration of Norm of Gaussian Random Vectors