IEOR-DRO SEMINAR: Nathan Kallus (Cornell)
IEOR-DRO SEMINAR: Nathan Kallus (Cornell)Uris Hall 331
Title: "Learning to Personalize from Observational and Behavioral Data"
Abstract: Personalization has long been central in machine learning, with successful applications in online news and product recommendation systems. A question of growing urgency is how to translate this success to emergent challenges such as personalized medicine. I will present a new machine learning toolset for building personalization models based on purely observational data, such as hospitals' electronic medical records (EMR), where the isolated effect of a treatment is hidden by confounding factors. This is important because, unlike electronic and online settings, in medicine and other settings, experimentation can be prohibitively small-scale, costly, dangerous, and unethical in comparison to passive data collection, which can be massive. The toolset, which includes learning algorithms and validation schemes, is based on a new reformulation of the personalization problem as a single learning task and I will demonstrate empirically that it provides significant advantages over the standard approaches in specific personalized medicine and policymaking contexts. I will then present a particular application to personalized diabetes management, where we use EMRs of Boston Medical Center to devise a pharmacological treatment algorithm for their diabetes patients that provides personalized care based on patient characteristics, disease progression, and treatment history and achieves a 4.8 mmol/mol reduction in HbA1C (which measures blood glucose) relative to standard of care in the instances where it differs. Finally, I will present new results on dynamic assortment personalization in the face of a highly heterogenous population and very many items, where I show how to use low rank models and convex optimization to make low-regret learning from purely behavioral choice data practically feasible.
This talk includes joint work with M Udell, D Bertsimas, A Weinstein, and Y Zhou.