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 paper studies systematic exploration for reinforcement learning (RL) with rich
observations and function approximation. We introduce contextual decision processes …

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

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 …

We present a new algorithm for the contextual bandit learning problem, where the learner
repeatedly takes one of K\emphactions in response to the observed\emphcontext, and …

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

Standard experimental designs are geared toward point estimation and hypothesis testing,
while bandit algorithms are geared toward in‐sample outcomes. Here, we instead consider …

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 …

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