Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Machine Learning
2022-11-03 v1 Machine Learning
Abstract
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with data and the need to run an optimization loop when using a non-Gaussian likelihood. In this paper, we focus on `fantasizing' batch acquisition functions that need the ability to condition on new fantasized data computationally efficiently. By using a sparse Dual GP parameterization, we gain linear scaling with batch size as well as one-step updates for non-Gaussian likelihoods, thus extending sparse models to greedy batch fantasizing acquisition functions.
Keywords
Cite
@article{arxiv.2211.01053,
title = {Fantasizing with Dual GPs in Bayesian Optimization and Active Learning},
author = {Paul E. Chang and Prakhar Verma and ST John and Victor Picheny and Henry Moss and Arno Solin},
journal= {arXiv preprint arXiv:2211.01053},
year = {2022}
}
Comments
In the 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems