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 …

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 …

An up-to-date account of the interplay between optimization and machine learning,
accessible to students and researchers in both communities. The interplay between …

We consider the adversarial linear contextual bandit problem, where the loss vectors are
selected fully adversarially and the per-round action set (ie the context) is drawn from a fixed …

We present methods for online linear optimization that take advantage of benign (as
opposed to worst-case) sequences. Specifically if the sequence encountered by the learner …

Bandit convex optimization is a special case of online convex optimization with partial
information. In this setting, a player attempts to minimize a sequence of adversarially …

We consider the task of accurately modeling strong human policies in multi-agent decision-
making problems, given examples of human behavior. Imitation learning is effective at …

We study sequential prediction problems in which, at each time instance, the forecaster
chooses a vector from a given finite set S⊆ Rd. At the same time, the opponent chooses a …