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Accelerated Bayesian Optimization through Weight-Prior Tuning
conference contribution
posted on 2020-01-01, 00:00 authored by Alistair ShiltonAlistair Shilton, Sunil GuptaSunil Gupta, Santu RanaSantu Rana, Pratibha Vellanki, Laurence Park, Cheng Li, Svetha VenkateshSvetha Venkatesh, Thomas DorinThomas Dorin, Alessandra SuttiAlessandra Sutti, David Rubin, Teo Slezak, Alireza VahidAlireza Vahid, Murray HeightBayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a Gaussian Process (GP) whose covariance is selected from a set of standard covariance functions. From a weight-space view, this models the objective as a linear function in a feature space implied by the given covariance πΎ, with an arbitrary Gaussian weight prior π°βΌππππππ π‘(0,π). In many practical applications there is data available that has a similar (covariance) structure to the objective, but which, having different form, cannot be used directly in standard transfer learning. In this paper we show how such auxiliary data may be used to construct a GP covariance corresponding to a more appropriate weight prior for the objective function. Building on this, we show that we may accelerate BO by modeling the objective function using this (learned) weight prior, which we demonstrate on both test functions and a practical application to short-polymer fibre manufacture.
History
Event
Artificial Intelligence and Statistics. Conference (2020 23rd : Online from Palermo, Italy)Volume
108Series
Proceedings of Machine Learning ResearchPagination
1 - 10Publisher
Addison-WesleyLocation
Online from Palermo, ItalyPlace of publication
[Palermo, Italy]Start date
2020-08-26End date
2020-08-28ISSN
2640-3498Language
engPublication classification
E Conference publication; E1 Full written paper - refereedEditor/Contributor(s)
S Chiappa, R CalandraTitle of proceedings
AISTATS 2020 : Proceedings of the 23rd International Conference on Artificial Intelligence and StatisticsUsage metrics
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