Speaker: Jaehyeok Shin

Abstract: Since Wald's pioneering work in 1947, the sequential testing problem has been thoroughly studied in which a single hypothesis is sequentially tested based on the streaming of data. Various testing methods based on the likelihood ratio technique have been proposed and analyzed since they are well-suited in the sequential setting and often have some optimalities. However, most of previous works have focused on asymptotic properties of the sequential procedures. In this talk, a recent advance in the non-asymptotic analysis of the sequential testing will be discussed. In particular, we will discuss the advantages of using overlapping hypotheses in the sequential setting and present lower bounds of the sample complexity for sequential tests with fixed confidence. In the end, a simple modification of the generalized likelihood ratio test is introduced which has a non-asymptotic upper bound of the sample complexity matching the lower bound.

Reference: Garivier and Kaufmann 2019 (https://arxiv.org/abs/1905.03495)