A1179
Title: Mathematical foundations of outcome weighted learning in precision medicine
Authors: Daohong Xiang - Zhejiang Normal University (China) [presenting]
Abstract: Outcome-weighted learning (OWL) is one of the most popular algorithms for estimating the optimal individualized treatment rules in precision medicine. The convergence theory of OWL for the cases of bounded and unbounded clinical outcomes is mainly studied. Fast learning rates of OWL associated with least square loss, exponential-hinge loss and r-norm SVM loss are derived explicitly.
