Dr. Susan Wei (University of North Carolina at Chapel Hill)

Title: Latent Supervised Learning

Abstract: Machine learning is a branch of artificial intelligence concerning the construction of systems that can learn from data. Machine learning algorithms can be placed along a spectrum according to the type of input available during training. The two main machine learning algorithms, unsupervised and supervised learning, occupy either end of this spectrum. 

In this talk I will overview some of my recent research on machine learning tasks that fall somewhere in the middle of this spectrum. I will primarily focus on a new machine learning task called latent supervised learning, where the goal is to learn a binary classifier from continuous training labels that serve as surrogates for the unobserved class labels. A specific model is investigated where the surrogate variable arises from a two-component Gaussian mixture with unknown means and variances, and the component membership is determined by a hyperplane in the covariate space. A data-driven sieve maximum likelihood estimator for the hyperplane is proposed. Extensions of the framework to survival data and applications to estimating treatment effect heterogeneity will also be discussed.