In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …

I believe that machine learning (ML) will have a dramatic impact on the field of economics
within a short time frame. Indeed, the impact of ML on economics is already well underway …

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 …

We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …

Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in
an infinite-horizon decision problem, now refers to a general class of reinforcement learning …

Constrained Bayesian Optimization with Noisy Experiments Page 1 Bayesian Analysis (2019)
14, Number 2, pp. 495–519 Constrained Bayesian Optimization with Noisy Experiments …

Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision
policies without needing to conduct exploration, which is often costly or otherwise infeasible …

We present an algorithm, HOMER, for exploration and reinforcement learning in rich
observation environments that are summarizable by an unknown latent state space. The …

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as
well as the exploration method used, particularly in the presence of rich heterogeneity or …