BIMSA > Mathematical Biology and Deep Learning Learning sub-Gaussianity
Learning sub-Gaussianity
Organizers
Seyed Mofidi , Wu Yue Yang
Speaker
Huiming Zhang
Time
Tuesday, December 17, 2024 9:00 AM - 10:30 AM
Venue
Online
Online
Zoom 518 868 7656 (BIMSA)
Abstract
In the era of AI, the variance-type parameters of sub-Gaussian distributions are of paramount importance in upper confidence bound bandit algorithms and non-asymptotic statistical inferences. However, directly estimating these parameters using the empirical moment generating function is infeasible. To address this, we suggest using the sub-Gaussian intrinsic moment norm achieved by maximizing a sequence of normalized moments. In practice, we offer an intuitive method for checking sub-Gaussian data with a finite sample size using the sub-Gaussian plot. Intrinsic moment norm can be robustly estimated via a simple plug-in approach. Our theoretical findings are also applicable to the multi-armed bandit scenario.