Seminario Datalab
Empirical Bernstein in smooth Banach spaces
Ponente: Diego Martínez Taboada (Carnegie Mellon University)Fecha: viernes 13 de diciembre de 2024 - 11:00Lugar: Aula Gris 2, ICMAT
Resumen:
Existing concentration bounds for bounded vector-valued random variables include extensions of the scalar Hoeffding and Bernstein inequalities. While the latter is typically tighter, it requires knowing a bound on the variance of the random variables. We derive a new vector-valued empirical Bernstein inequality, which makes use of an empirical estimator of the variance instead of the true variance. The bound holds in 2-smooth separable Banach spaces, which include finite dimensional Euclidean spaces and separable Hilbert spaces. The resulting confidence sets are instantiated for both the batch setting (where the sample size is fixed) and the sequential setting (where the sample size is a stopping time). The confidence set width asymptotically exactly matches that achieved by Bernstein in the leading term. The method and supermartingale proof technique combine several tools of Pinelis (1994) and Waudby-Smith and Ramdas (2024).
