Consider the problem of estimating the Shannon entropy of a distribution over k elements
from n independent samples. We show that the minimax mean-square error is within the …

Outlier hypothesis testing is studied in a universal setting. Multiple sequences of
observations are collected, a small subset of which are outliers. A sequence is considered …

This survey provides an exposition of a suite of techniques based on the theory of
polynomials, collectively referred to as polynomial methods, which have recently been …

We revisit the problem of statistical sequence matching between two databases of
sequences initiated by Unnikrishnan,(2015) and derive theoretical performance guarantees …

Motivated by real-world machine learning applications, we analyse approximations to the
non-asymptotic fundamental limits of statistical classification. In the binary version of this …

Given $ n $ observations from two balanced classes, consider the task of labeling an
additional $ m $ inputs that are known to all belong to\emph {one} of the two classes …

Motivated by real-world machine learning applications, we consider a statistical
classification task in a sequential setting where test samples arrive sequentially. In addition …

This paper considers an ML inspired approach to hypothesis testing known as
classifier/classification-accuracy testing (CAT). In CAT, one first trains a classifier by feeding …

Consider the problem of binary hypothesis testing. Given Z coming from either or, to decide
between the two with small probability of error it is sufficient, and in many cases necessary …

We consider Bayesian multiple statistical classification problem in the case where the
unknown source distributions are estimated from the labeled training sequences, then the …