323 citations · 2.7k across the 27 of their papers we have counts for
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stat.ME2020
Active Learning for Deep Gaussian Process Surrogates
Annie Sauer, Robert B. Gramacy, David Higdon
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 ch…
stat.ME2020★ 5 cited
Analyzing Stochastic Computer Models: A Review with Opportunities
Evan Baker, Pierre Barbillon, Arindam Fadikar +10
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a…