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 consider a statistical
classification task in a sequential setting where test samples arrive sequentially. In addition …

The focus of this paper is on binary classification of a sequentially observed stream of iid
samples, based on sequentially observed empirical statistics. The decision maker (classifier) …

We revisit the multiple classification problem and propose a two-phase test, where each
phase is a fixed-length test and the second-phase proceeds only if a reject option is decided …

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

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 revisit the binary classification problem where the generating distribution under each
hypothesis is unknown and propose a two-phase test, where each phase is a fixed-length …

Consider a distributed detection problem in which the underlying distributions of the
observations are unknown; instead of these distributions, noisy versions of empirically …

We propose a universal classifier for binary Neyman–Pearson classification where the null
distribution is known, while only a training sequence is available for the alternative …

We revisit the outlier hypothesis testing framework of Li et al.(TIT 2014) and derive
fundamental limits for the optimal test under the generalized Neyman-Pearson criterion. In …