We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action
spaces where conventional importance-weighting approaches suffer from excessive …

Ranking interfaces are everywhere in online platforms. There is thus an ever growing
interest in their Off-Policy Evaluation (OPE), aiming towards an accurate performance …

We study off-policy evaluation (OPE) in the problem of slate contextual bandits where a
policy selects multi-dimensional actions known as slates. This problem is widespread in …

We study off-policy learning (OPL) of contextual bandit policies in large discrete action
spaces where existing methods--most of which rely crucially on reward-regression models …

Approaches to recommendation are typically evaluated in one of two ways:(1) via a
(simulated) online experiment, often seen as the gold standard, or (2) via some offline …

Evaluating the value of a hypothetical target policy with only a logged dataset is important
but challenging. On the one hand, it brings opportunities for safe policy improvement under …

This paper introduces SCOPE-RL, a comprehensive open-source Python software designed
for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection …

The Off-Policy Evaluation (OPE) problem consists of evaluating the performance of
counterfactual policies with data collected by another one. This problem is of utmost …

We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits
(CCB), where a policy selects a subset in the action space. For example, it might choose a …

In this paper, we study the problem of estimator selection and hyper-parameter tuning in off-
policy evaluation. Although cross-validation is the most popular method for model selection …