2016
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
AISTATS 2016poster
We consider the problem of learning mixtures of generalized linear models (GLM) which arise in classification and regression problems. Typical learning approaches such as expectation maximization (EM) or variational Bayes can get stuck in spurious local optima. In contrast, we present a tenso…