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 …

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

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 …

What price should be offered to a worker for a task in an online labor market? How can one
enable workers to express the amount they desire to receive for the task completion …

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 …

We consider the linear contextual bandit problem with resource consumption, in addition to
reward generation. In each round, the outcome of pulling an arm is a reward as well as a …

We study online learning problems in which a decision maker wants to maximize their
expected reward without violating a finite set of $ m $ resource constraints. By casting the …

In budget–limited multi–armed bandit (MAB) problems, thelearner's actions are costly and
constrained by a fixed budget. Consequently, an optimal exploitation policy may not be …