Maximizing acquisition functions for Bayesian optimization.

File Description SizeFormat 
1805.10196v1.pdfWorking paper5.09 MBAdobe PDFView/Open
Title: Maximizing acquisition functions for Bayesian optimization.
Authors: Wilson, JT
Hutter, F
Deisenroth, MP
Item Type: Working Paper
Abstract: Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide the search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions are frequently non-trivial to optimize. This statement is especially true when evaluating queries in parallel, where acquisition functions are routinely non-convex, high-dimensional, and intractable. We present two modern approaches for maximizing acquisition functions that exploit key properties thereof, namely the differentiability of Monte Carlo integration and the submodularity of parallel querying.
Issue Date: 25-May-2018
URI: http://hdl.handle.net/10044/1/63073
Publisher: arxiv
Copyright Statement: © 2018 The Author(s).
Keywords: stat.ML
cs.LG
Appears in Collections:Faculty of Engineering
Computing



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx