Roland Speicher
Publishes 1 feed
Lately
RM+ML: 15. Spiked Signal-Plus-Noise Model
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 14. Proof of Marchenko-Pastur: Stieltjes Inversion Formula
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 13. Proof of Marchenko-Pastur: Equation for Stieltjes Transform
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 12. Preparations for Proof of Marchenko-Pastur Law
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 11. The Marchenko-Pastur Law for Wishart Matrices
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 10. Proof of Concentration of Largest Eigenvalue
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 9. Wishart Random Matrices and Concentration of Largest Eigenvalue
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 8. General Remarks on Linear and Non-Linear Concentration Inequalities
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 7. Proof of Non-Linear Concentration for Gaussian Random Vectors
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 6. Non-Linear Concentration of Gaussian Random Vectors for Lipschitz Functions.
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 5. Exponential Concentration of Norm of Gaussian Random Vectors
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
RM+ML: 4. Gaussian Random Vectors and Concentration of Their Norm
High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) ·
Everything on this page was read from markup Roland Speicher published — a rel="me" link, an h-card, or the feed’s own author element. Nothing was inferred from anywhere else. To correct or remove it, get in touch. Machine-readable: JSON