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 …

A fundamental challenge in contextual bandits is to develop flexible, general-purpose
algorithms with computational requirements no worse than classical supervised learning …

We consider the problem faced by a firm that receives highly differentiated products in an
online fashion. The firm needs to price these products to sell them to its customer base …

A major research direction in contextual bandits is to develop algorithms that are
computationally efficient, yet support flexible, general-purpose function approximation …

We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal
in terms of dynamic regret. Specifically, our algorithm achieves $\mathcal {O}(\min\{\sqrt …

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 present three new algorithms for constructing differentially private synthetic data—a
sanitized version of a sensitive dataset that approximately preserves the answers to a large …

Most contextual bandit algorithms minimize regret against the best fixed policy, a
questionable benchmark for non-stationary environments that are ubiquitous in applications …