What is so amazing about Wigner's semicircle law?
5:57
What is so amazing about Wigner's semicircle law?
The curse and the blessing of high dimensions
19:47
The curse and the blessing of high dimensions
RM+ML: 29. Free Probability Theory and Linearization of Non-Linear Problems
1:10:11
RM+ML: 29. Free Probability Theory and Linearization of Non-Linear Problems
RM+ML: 28. Properties of the Neural Tangent Kernel
53:13
RM+ML: 28. Properties of the Neural Tangent Kernel
RM+ML: 27. Time Evolution of Learning and Neural Tangent Kernel
1:25:57
RM+ML: 27. Time Evolution of Learning and Neural Tangent Kernel
RM+ML: 26. Gradient Descent for Linear Regression
1:13:12
RM+ML: 26. Gradient Descent for Linear Regression
RM+ML: 25. Gaussian Equivalence Principle for Non-Linear Random Features
16:34
RM+ML: 25. Gaussian Equivalence Principle for Non-Linear Random Features
RM+ML: 24. Calculation of the Random Feature Eigenvalue Distribution
1:36:12
RM+ML: 24. Calculation of the Random Feature Eigenvalue Distribution
RM+ML: 23. Cumulants and Their Properties and Uses
1:19:34
RM+ML: 23. Cumulants and Their Properties and Uses
RM+ML: 22. Resolvent Method and Cumulant Expansion
1:06:15
RM+ML: 22. Resolvent Method and Cumulant Expansion
RM+ML: 21. Another Proof of Marchenko-Pastur -- Via Stein's Identity
57:40
RM+ML: 21. Another Proof of Marchenko-Pastur -- Via Stein's Identity
RM+ML: 20. Asymptotic Eigenvalue Distribution in the Random Feature Model
37:06
RM+ML: 20. Asymptotic Eigenvalue Distribution in the Random Feature Model
RM+ML: 19. General Remarks on Random Feature Model
17:08
RM+ML: 19. General Remarks on Random Feature Model
RM+ML: 18. Double Descent and Linear Regression: Under-Determined Case
53:38
RM+ML: 18. Double Descent and Linear Regression: Under-Determined Case
RM+ML: 17. Double Descent and Linear Regression: Over-Determined Case
1:00:54
RM+ML: 17. Double Descent and Linear Regression: Over-Determined Case
RM+ML: 16. Proof of the Signal-Plus-Noise Theorem
22:59
RM+ML: 16. Proof of the Signal-Plus-Noise Theorem
RM+ML: 15. Spiked Signal-Plus-Noise Model
54:21
RM+ML: 15. Spiked Signal-Plus-Noise Model
RM+ML: 14. Proof of Marchenko-Pastur: Stieltjes Inversion Formula
31:49
RM+ML: 14. Proof of Marchenko-Pastur: Stieltjes Inversion Formula
RM+ML: 13. Proof of Marchenko-Pastur: Equation for Stieltjes Transform
24:31
RM+ML: 13. Proof of Marchenko-Pastur: Equation for Stieltjes Transform
RM+ML: 12. Preparations for Proof of Marchenko-Pastur Law
1:07:18
RM+ML: 12. Preparations for Proof of Marchenko-Pastur Law
RM+ML: 11. The Marchenko-Pastur Law for Wishart Matrices
59:39
RM+ML: 11. The Marchenko-Pastur Law for Wishart Matrices
RM+ML: 10. Proof of Concentration of Largest Eigenvalue
26:26
RM+ML: 10. Proof of Concentration of Largest Eigenvalue
RM+ML: 9. Wishart Random Matrices and Concentration of Largest Eigenvalue
1:05:03
RM+ML: 9. Wishart Random Matrices and Concentration of Largest Eigenvalue
RM+ML: 8. General Remarks on Linear and Non-Linear Concentration Inequalities
43:24
RM+ML: 8. General Remarks on Linear and Non-Linear Concentration Inequalities
RM+ML: 7. Proof of Non-Linear Concentration for Gaussian Random Vectors
1:05:20
RM+ML: 7. Proof of Non-Linear Concentration for Gaussian Random Vectors
RM+ML: 6. Non-Linear Concentration of Gaussian Random Vectors for Lipschitz Functions.
1:30:06
RM+ML: 6. Non-Linear Concentration of Gaussian Random Vectors for Lipschitz Functions.
RM+ML: 5. Exponential Concentration of Norm of Gaussian Random Vectors
1:23:07
RM+ML: 5. Exponential Concentration of Norm of Gaussian Random Vectors
RM+ML: 4. Gaussian Random Vectors and Concentration of Their Norm
1:24:44
RM+ML: 4. Gaussian Random Vectors and Concentration of Their Norm
RM+ML: 3. Concentration of Volumes
1:19:05
RM+ML: 3. Concentration of Volumes
RM+ML: 2. Volumes in High Dimensions
1:27:39
RM+ML: 2. Volumes in High Dimensions