Closing the loop in Machine Learning: Learning to optimize with decision dependent data

Learning algorithms are increasingly being deployed in a variety of real world systems with other autonomous decision processes and human decision-makers. Importantly, in many settings humans react to the decisions algorithms make. This calls into question the following classically held tenet in supervised machine learning: when it is arduous to model a phenomenon, observations thereof are representative samples from some static or otherwise independent distribution. Without taking such reactions into consideration at the time of design, machine learning algorithms are doomed to result in unintended consequences such as reinforcing institutional bias or incentivizing gaming or collusion. In this talk, we discuss several directions of research along which we have made progress towards closing the loop in ML including robustness to model misspecification in capturing strategic behavior, decision-dependent learning in the presence of competition ('multiplayer performative prediction'), and dynamic decision-dependent learning wherein the data distribution may drift in time. Open questions will be posed towards the end of the talk.