Decision making with limited feedback

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Danielle Ensign, Frielder Sorelle, Neville Scott, Scheidegger Carlos, Venkatasubramanian Suresh ;
Proceedings of Algorithmic Learning Theory, PMLR 83:359-367, 2018.

Abstract

When models are trained for deployment in decision-making in various real-world settings, they are typically trained in batch mode. Historical data is used to train and validate the models prior to deployment. However, in many settings, \emph{feedback} changes the nature of the training process. Either the learner does not get full feedback on its actions, or the decisions made by the trained model influence what future training data it will see. In this paper, we focus on the problems of recidivism prediction and predictive policing. We present the first algorithms with provable regret for these problems, by showing that both problems (and others like these) can be abstracted into a general reinforcement learning framework called partial monitoring. We also discuss the policy implications of these solutions.

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