CaltechAUTHORS
  A Caltech Library Service

Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design

Yang, Kevin K. and Chen, Yuxin and Lee, Alycia and Yue, Yisong (2019) Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design. Proceedings of Machine Learning Research, 89 . pp. 3410-3419. ISSN 1938-7228. https://resolver.caltech.edu/CaltechAUTHORS:20190905-154254753

[img] PDF - Published Version
See Usage Policy.

3311Kb
[img] PDF - Submitted Version
See Usage Policy.

2138Kb
[img] PDF - Supplemental Material
See Usage Policy.

745Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20190905-154254753

Abstract

In many high-throughput experimental design settings, such as those common in biochemical engineering, batched queries are often more cost effective than one-by-one sequential queries. Furthermore, it is often not possible to directly choose items to query. Instead, the experimenter specifies a set of constraints that generates a library of possible items, which are then selected stochastically. Motivated by these considerations, we investigate \emph{Batched Stochastic Bayesian Optimization} (BSBO), a novel Bayesian optimization scheme for choosing the constraints in order to guide exploration towards items with greater utility. We focus on \emph{site-saturation mutagenesis}, a prototypical setting of BSBO in biochemical engineering, and propose a natural objective function for this problem. Importantly, we show that our objective function can be efficiently decomposed as a difference of submodular functions (DS), which allows us to employ DS optimization tools to greedily identify sets of constraints that increase the likelihood of finding items with high utility. Our experimental results show that our algorithm outperforms common heuristics on both synthetic and two real protein datasets.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://proceedings.mlr.press/v89/yang19c.htmlPublisherArticle
https://arxiv.org/abs/1904.08102arXivDiscussion Paper
ORCID:
AuthorORCID
Yang, Kevin K.0000-0001-9045-6826
Lee, Alycia0000-0001-5972-807X
Yue, Yisong0000-0001-9127-1989
Additional Information:© 2019 by the author(s). This work was supported in part by the Donna and Benjamin M. Rosen Bioengineering Center, the U.S. Army Research Office Institute for Collaborative Biotechnologies, NSF Award #1645832, Northrop Grumman, Bloomberg, PIMCO, and a Swiss NSF Early Mobility Postdoctoral Fellowship.
Group:Rosen Bioengineering Center
Funders:
Funding AgencyGrant Number
Donna and Benjamin M. Rosen Bioengineering CenterUNSPECIFIED
Army Research Office (ARO)UNSPECIFIED
NSFCNS-1645832
Northrop Grumman CorporationUNSPECIFIED
Bloomberg Data ScienceUNSPECIFIED
PIMCOUNSPECIFIED
Swiss National Science Foundation (SNSF)UNSPECIFIED
Record Number:CaltechAUTHORS:20190905-154254753
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190905-154254753
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98455
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:06 Sep 2019 14:44
Last Modified:05 Mar 2020 18:05

Repository Staff Only: item control page