We study the performance of Machine Learning (ML) classification techniques. Leveraging
the theory of large deviations, we provide the mathematical conditions for a ML classifier to …

This paper considers the problem of online clustering with bandit feedback. A set of arms (or
items) can be partitioned into various groups that are unknown. Within each group, the …

We study the problem of identifying the best arm in a multi-armed bandit environment when
each arm is a time-homogeneous and ergodic discrete-time Markov process on a common …

In this paper, we consider a multi-armed bandit in which each arm is a Markov process
evolving on a finite state space. The state space is common across the arms, and the arms …

We study best arm identification in a restless multi-armed bandit setting with finitely many
arms. The discrete-time data generated by each arm forms a homogeneous Markov chain …

Abstract The Global Navigation Satellite System/Strapdown Inertial Navigation System
(GNSS/SINS) integrated navigation system is an important technology for UAV …

We develop a general framework for clustering and distribution matching problems with
bandit feedback. We consider a $ K $-armed bandit model where some subset of $ K $ arms …

We study best arm identification in a restless multi-armed bandit setting with finitely many
arms. The discrete-time data generated by each arm forms a homogeneous Markov chain …

The problem of sequential anomaly detection and identification is considered in the
presence of a sampling constraint. Specifically, multiple data streams are generated by …

In this paper, we introduce a new framework that can be used for evaluating the validity and
the performance of machine learning models on manifold-valued data. More particularly, two …