This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

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

Reinforcement learning (RL) methods have been shown to be capable of learning intelligent
behavior in rich domains. However, this has largely been done in simulated domains without …

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

We develop a model selection approach to tackle reinforcement learning with adversarial
corruption in both transition and reward. For finite-horizon tabular MDPs, without prior …

We study bandit model selection in stochastic environments. Our approach relies on a
master algorithm that selects between candidate base algorithms. We develop a master …

We introduce data-driven decision-making algorithms that achieve state-of-the-art dynamic
regret bounds for a collection of nonstationary stochastic bandit settings. These settings …

In offline reinforcement learning (RL), a learner leverages prior logged data to learn a good
policy without interacting with the environment. A major challenge in applying such methods …

We propose a framework for model selection by combining base algorithms in stochastic
bandits and reinforcement learning. We require a candidate regret bound for each base …

Materials development is slow and expensive, taking decades from inception to fielding. For
materials research at extremes, the situation is even more demanding, as the desired …