STA 290 Seminar: Han Liu (Princeton University)

STA 290 Seminar Series

Thursday, May 26th, 4:10pm, MSB 1147 (Colloquium Room)

Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)

Speaker:          Han Liu (Princeton University)

Title:                “Nonparametric Graphical Model”

Abstract:          We consider the problem of learning the structure of a non-Gaussian graphical model. We introduce two strategies for constructing tractable nonparametric graphical model families. One approach is through semiparametric extension of the Gaussian or exponential family graphical models that allows arbitrary graphs. Another approach is to restrict the family of allowed graphs to be acyclic, enabling the use of fully nonparametric density estimation in high dimensions. These two approaches can both be viewed as adding structural regularization to the a general pairwise nonparametric Markov random field and reflect an interesting tradeoff of model flexibility with structural complexity. In terms of graph estimation, these methods achieve the optimal parametric rates of convergence.