Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It

arXiv:2404.15084v1 Announce Type: new
Abstract: There has been a growing interest in off-policy evaluation in the literature such as recommender systems and personalized medicine. We have so far seen significant progress in developing estimators aimed at accurately estimating the effectiveness of counterfactual policies based on biased logged data. However, there are many cases where those estimators are used not only to evaluate the value of decision making policies but also to search for the best hyperparameters from a large candidate …

abstract arxiv counterfactual cs.lg deal evaluation hyperparameter literature medicine optimization personalized policies policy progress recommender systems systems type