This tutorial serves as an introduction to recently developed non-asymptotic methods in the
theory of-mainly linear-system identification. We emphasize tools we deem particularly …

In this work, we study statistical learning with dependent ($\beta $-mixing) data and square
loss in a hypothesis class $\mathscr {F}\subset L_ {\Psi_p} $ where $\Psi_p $ is the norm …

A driving force behind the diverse applicability of modern machine learning is the ability to
extract meaningful features across many sources. However, many practical domains involve …

In this paper we study the asymptotics of linear regression in settings where the covariates
exhibit a linear dependency structure, departing from the standard assumption of …

2dWt,(1) whereU is some smooth function and (Wt) t≥ 0 is a Wiener process. Under some
regularity condition, the unique invariant distribution of (1) is given by π (x)∝ e− U (x) …