A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

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 …

In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …

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 …

We study an online linear classification problem in which the data is generated by strategic
agents who manipulate their features in an effort to change the classification outcome. In …

We develop a novel and generic algorithm for the adversarial multi-armed bandit problem
(or more generally the combinatorial semi-bandit problem). When instantiated differently, our …

We study the problem of combining multiple bandit algorithms (that is, online learning
algorithms with partial feedback) with the goal of creating a master algorithm that performs …

A foundational problem in reinforcement learning and interactive decision making is to
understand what modeling assumptions lead to sample-efficient learning guarantees, and …

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 …