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 …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

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

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 …

Increasingly, organisations flexibly outsource work on a temporary basis to a global
audience of workers. This so-called crowdsourcing has been applied successfully to a range …

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 the multi-armed bandit problem with multiple plays and a budget constraint for
both the stochastic and the adversarial setting. At each round, exactly K out of N possible …