We propose a novel evaluation methodology for assessing off-policy learning methods in
contextual bandits. In particular, we provide a way to use data from any given Randomized …

Evaluating novel contextual bandit policies using logged data is crucial in applications
where exploration is costly, such as medicine. But it usually relies on the assumption of no …

Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in
healthcare and the tech industry. They involve online learning algorithms that adaptively …

We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic
policies in contextual bandits with continuous action spaces. Our work is motivated by …

Abstract Off-Policy Evaluation (OPE) in contextual bandits is crucial for assessing new
policies using existing data without costly experimentation. However, current OPE methods …

We propose an estimator and confidence interval for computing the value of a policy from off-
policy data in the contextual bandit setting. To this end we apply empirical likelihood …

Most off-policy evaluation methods for contextual bandits have focused on the expected
outcome of a policy, which is estimated via methods that at best provide only asymptotic …

We present and prove properties of a new offline policy evaluator for an exploration learning
setting which is superior to previous evaluators. In particular, it simultaneously and correctly …

We address policy learning with logged data in contextual bandits. Current offline-policy
learning algorithms are mostly based on inverse propensity score (IPS) weighting requiring …

Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-
commerce, healthcare, and policymaking because they can both improve outcomes for …