"Efficient Sparse Bayesian Learning using Spike-and-Slab Priors" by Syed Abbas Zilqurnain Naqvi Zilqurnain Naqvi

Date of Award

January 2016

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Yuan Qi

Committee Member 1

Charles A Bouman

Committee Member 2

Jennifer Neville

Committee Member 3

David F Gleich

Committee Member 4

Ninghui Li

Abstract

In the context of statistical machine learning, sparse learning is a procedure that seeks a reconciliation between two competing aspects of a statistical model: good predictive power and interpretability. In a Bayesian setting, sparse learning methods invoke sparsity inducing priors to explicitly encode this tradeoff in a principled manner.

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