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Activity Number: 506
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #321498 View Presentation
Title: Free Lunches with Sparse Bayesian Nonparametric Learning: A Probabilistic Exploration of Lower Dimensional Structure Discovery with Sparse High-Dimensional Data
Author(s): Anjishnu Banerjee*
Companies: Medical College of Wisconsin
Keywords: Bayesian ; Nonparametric ; Random projection ; Gaussian process ; Scalable ; Approximation accuracy

Bayesian nonparametric inference is often too expensive for massive datasets to be practically useful. Many current Bayesian computation ideas deal with discovery of a underlying lower dimensional structure. In this context, we propose a new class of algorithms using randomized inference - which show that explicit dimension reduction is often not necessary, depending on the underlying smoothness of the prior processes, we may get away with random projections. We describe a methodology and associated computation that provides an extremely scalable and generic scheme for randomized inference with Bayesian algorithms, with strong theoretical justification. We provide several examples, including in the context of Gaussian process, Bayesian classification and Bayesian random forests, with simulated and real data sets, where we demonstrate the superiority of our approach, not only from an efficiency point of view, but also with respect to increased accuracy of predictions, which are probably due to the increased stability of the resultant inference.

Authors who are presenting talks have a * after their name.

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