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Activity Number: 104 - Advances in Bayesian Analysis of Computer Models
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #322531
Title: Using Gradient Descent for Gaussian Process Prediction
Author(s): Matthew Plumlee*
Companies: Northwestern University
Keywords: kriging; computer experiments

Gaussian processes are popular tools for Bayesian prediction that have been shown to be orders of magnitude more accurate that modern competitors on a host of prediction tasks. However, the computational cost of fitting them can be daunting. Inspired by the recent deployments of large scale optimization in deep learning, this talk illustrates how carefully written optimization problems can be used to replace the usual matrix decomposition used to fit Gaussian process predictors.

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

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