Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

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 …

Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a
variety of ad hoc border control protocols to allow for non-essential travel while safeguarding …

In many sequential decision-making problems, the goal is to optimize a utility function while
satisfying a set of constraints on different utilities. This learning problem is formalized …

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

We consider the problem faced by a firm that receives highly differentiated products in an
online fashion. The firm needs to price these products to sell them to its customer base …

We study online learning problems in which a decision maker has to take a sequence of
decisions subject to $ m $ long-term constraints. The goal of the decision maker is to …

We study the problem of learning shared structure across a sequence of dynamic pricing
experiments for related products. We consider a practical formulation in which the unknown …

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 …

We introduce the online stochastic Convex Programming (CP) problem, a very general
version of stochastic online problems which allows arbitrary concave objectives and convex …