Local laws of sample covariance matrices beyond the separable case

Elliot Paquette (McGill U)

Apr 14. 2026, 11:30 — 12:30

Sample covariance matrices are among the most fundamental objects in random matrix theory and statistics. In this talk, I'll discuss recent work identifying the assumptions on random vectors that allow local laws to hold for their sample covariance matrices — these are matrices with iid rows sampled from a single distribution of random vector.

The well--studied separable form g=Xw, where w has independent entries and X is a fixed matrix, was solved, essentially completely, by Knowles and Yin.  However, the separable case just scratches the surface of what should be true. We ask: what assumptions on g are really needed?  We show that concentration of quadratic forms suffices for an optimal averaged local law, while a structural condition on cumulant tensors—interpolating between independence and generic dependence—suffices for the full anisotropic local law.

I'll discuss key examples where our assumptions can be verified: sign-invariant vectors, the 'random features model’, and some examples of spin-glass type. We will also outline limitations of the method, and suggest some key open problems.

Joint with Jack Ma (Yale), Zhou Fan (Yale), Zhichao Wang (Berkeley)

Further Information
Venue:
ESI Boltzmann Lecture Hall
Associated Event:
Random Matrices and Operators (Workshop)
Organizer(s):
Nathanael Berestycki (U of Vienna)
Paul Bourgade (CIMS, New York)
Giorgio Cipolloni (U Tor Vergata, Rome)