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Statistics Seminar Series: Fang Liu, PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models

When: Wednesday, February 20, 2019
11:00 AM - 12:00 PM Central

Where: 2006 Sheridan Road, B02, Evanston, IL 60208 map it

Audience: Faculty/Staff - Post Docs/Docs - Graduate Students

Contact: Kisa Kowal   (847) 491-3974

Group: Department of Statistics

Category: Academic


Department of Statistics Winter 2019 Seminar Series

Talk Title: PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models

Speaker: Fang Liu, Associate Professor, Department of Applied and Computational Mathematics and Statistics, University of Notre Dame

Time: 11:00am

Abstract: We propose PANDA, an AdaPtive Noise Augmentation technique to regularize estimating and constructing single and multiple undirected graphical models (UGMs). PANDA iteratively solves MLEs given noise augmented data in the regression-based framework until convergence. The noises can be designed to achieve various regularization effects on graph estimation, such as lasso, group lasso, ridge and elastic net, among others. When PANDA is used for constructing multiple graph simultaneously, two types of noises are augmented. The first type is to regularize the estimation of each graph while the second type promotes either the structural similarities (joint group lasso), or numerical similarities (joint fused ridge), among the edges in the same position across multiple graphs. We establish theoretically that the noise-augmented loss functions and its minimizer converge almost surely to the expected penalized loss function and its minimizer, respectively. We also derive the asymptotic distributions and inferences for the regularized regression coefficients through PANDA in the setting of GLMs. PANDA can be easily programmed in any standard software without resorting to complicated optimization techniques. We apply PANDA to the autism spectrum disorder data to construct a mixed-node graph, and a real-life lung cancer microarray data to simultaneously construct four protein networks.

Location: Department of Statistics, Room B02, 2006 Sheridan Rd, Evanston 60208

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