Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

We consider online allocation problems with concave revenue functions and resource
constraints, which are central problems in revenue management and online advertising. In …

This book is intended as a text covering the central concepts of practical optimization
techniques. It is designed for either self-study by professionals or classroom work at the …

Multi-armed bandit problems are the predominant theoretical model of exploration-
exploitation tradeoffs in learning, and they have countless applications ranging from medical …

Matching is a classic problem with a rich history and a significant impact, both on the theory
of algorithms and in practice. Recently there has been a surge of interest in the online …

Revenue management can be defined as a data-driven, computerized system to support the
tactical pricing of perishable assets at the micro-market level to maximize expected …

We explore the problem of assigning heterogeneous tasks to workers with different,
unknown skill sets in crowdsourcing markets such as Amazon Mechanical Turk. We first …

Motivated by the availability of real-time data on customer characteristics, we consider the
problem of personalizing the assortment of products for each arriving customer. Using actual …

In this paper, we consider a very general model for exploration-exploitation tradeoff which
allows arbitrary concave rewards and convex constraints on the decisions across time, in …

We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-armed
bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a …