Abstract Models of many real-life applications, such as queueing models of communication
networks or computing systems, have a countably infinite state-space. Algorithmic and …

We consider a long-term average profit–maximizing admission control problem in an M/M/1
queuing system with unknown service and arrival rates. With a fixed reward collected upon …

Approximate Bayesian Computation (ABC) enables statistical inference in simulatorbased
models whose likelihoods are difficult to calculate but easy to simulate from. ABC constructs …

This letter presents and analyzes an adaptive data-driven controller that learns the optimal
processing rate in a multi-unit processing network in the presence of disturbances. We …

We consider admission control for a first-in first-out single class single server queueing
model with Poisson arrivals and exponential service times. Specifically, there is a dispatcher …

Models of many real-life applications, such as queueing models of communication networks
or computing systems, have a countably infinite state-space. Algorithmic and learning …

Recently, reinforcement learning methodologies have been applied to solve sequential
decision-making problems in various fields, such as robotics and autonomous control …

This thesis studies two online learning problems in which the efficiency of the proposed
strategies is studied in terms of their regret. The first problem deals with designing learning …

We consider problems related to design and analysis of flexible server systems. On the
analysis side, we study the stability properties of the X-model under parameter agnostic …