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Activity Number: 60 - Invited E-Poster Session II
Type: Invited
Date/Time: Sunday, August 8, 2021 : 6:45 PM to 7:30 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #317339
Title: Active Learning for Deep Gaussian Process Surrogates
Author(s): Robert B Gramacy* and Annie Sauer and David Higdon
Companies: Virginia Tech and Virginia Tech and Virginia Tech
Keywords: emulator; computer experiment; sequential design; deep learning
Abstract:

Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning (ML) for their non-stationary flexibility and ability to cope with abrupt regime changes in training data. Here we explore DGPs as surrogates for computer simulation experiments whose response surfaces exhibit similar characteristics. In particular, we transport a DGP's automatic warping of the input space and full uncertainty quantification (UQ), via a novel elliptical slice sampling (ESS) Bayesian posterior inferential scheme, through to active learning (AL) strategies that distribute runs non-uniformly in the input space -- something an ordinary (stationary) GP could not do. Building up the design sequentially in this way allows smaller training sets, limiting both expensive evaluation of the simulator code and mitigating cubic costs of DGP inference. When training data sizes are kept small through careful acquisition, and with parsimonious layout of latent layers, the framework can be both effective and computationally tractable. Our deepgp package, on CRAN, is illustrated on simulation data and two real computer experiments of varying input dimensionality.


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

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