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Activity Number: 69
Type: Topic Contributed
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #315692 View Presentation
Title: Speeding Up Neighborhood Searches in Local Gaussian Process Fitting of Large-Scale Computer Experiments
Author(s): Ben Haaland* and Chih-Li Sung
Companies: Georgia Tech, ISyE and Georgia Tech, ISyE
Keywords: computer experiment ; local Gaussian process ; subsampling ; big data
Abstract:

Local Gaussian process fitting is a promising tool for predicting the output of very large-scale computer experiments. Predictions at distinct locations are based on distinct sub-samples of the full dataset, making the predictive task fully parallelizable. Efficient and scalable selection of the sub-sample to be used for prediction at a particular location remains a potential bottleneck. While it can be shown that a K-nearest-neighbor approach is not optimal, here we show that data locations which are sufficiently distant from the location of interest and the current sub-sample have arbitrarily small potential to reduce the predictive variance. Further refinements of the set of candidate data locations are possible via a projection into (residual) feature space. This feature space reveals a balance between nearby data locations and data locations along one-dimensional slow manifolds through the location of interest. An efficient and scalable sub-sample selection algorithm is presented.


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