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arXiv · 1712.02902

Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start

Valerio Perrone·Rodolphe Jenatton·Matthias Seeger·Cedric Archambeau
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Abstract

Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization. Typically, BO is powered by a Gaussian process (GP), whose algorithmic complexity is cubic in the number of evaluations. Hence, GP-based BO cannot leverage large amounts of past or related function evaluations, for example, to warm start the BO procedure. We develop a multiple adaptive Bayesian linear regression model as a scalable alternative whose complexity is linear in the number of observations. The multiple Bayesian linear regression models are coupled through a shared feedforward neural network, which learns a joint representation and transfers knowledge across machine learning problems.

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Valerio Perrone, Rodolphe Jenatton, Matthias Seeger, Cedric Archambeau. 2017-12-08. Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start. https://arxiv.org/abs/1712.02902

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