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 …

This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

Online learning is a well established learning paradigm which has both theoretical and
practical appeals. The goal of online learning is to make a sequence of accurate predictions …

Many applications require optimizing an unknown, noisy function that is expensive to
evaluate. We formalize this task as a multi-armed bandit problem, where the payoff function …

We provide an information-theoretic analysis of Thompson sampling that applies across a
broad range of online optimization problems in which a decision-maker must learn from …

How should we design experiments to maximize performance of a complex system, taking
into account uncontrollable environmental conditions? How should we select relevant …

In the classical stochastic k-armed bandit problem, in each of a sequence of T rounds, a
decision maker chooses one of k arms and incurs a cost chosen from an unknown …

This paper studies the evaluation of policies that recommend an ordered set of items (eg, a
ranking) based on some context---a common scenario in web search, ads, and …

In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence of choices.
In each round it chooses from a time-invariant set of alternatives and receives the payoff …