{"id":4624,"date":"2025-06-16T07:02:29","date_gmt":"2025-06-16T07:02:29","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/06\/16\/2506-11456\/"},"modified":"2025-06-16T07:02:29","modified_gmt":"2025-06-16T07:02:29","slug":"2506-11456","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/06\/16\/2506-11456\/","title":{"rendered":"Fast Bayesian Optimization of Function Networks with Partial Evaluations"},"content":{"rendered":"

Fast Bayesian Optimization of Function Networks with Partial Evaluations
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arXiv:2506.11456v1 Announce Type: new
\nAbstract: Bayesian optimization of function networks (BOFN) is a framework for optimizing expensive-to-evaluate objective functions structured as networks, where some nodes’ outputs serve as inputs for others. Many real-world applications, such as manufacturing and drug discovery, involve function networks with additional properties – nodes that can be evaluated independently and incur varying costs. A recent BOFN variant, p-KGFN, leverages this structure and enables cost-aware partial evaluations, selectively querying only a subset of nodes at each iteration. p-KGFN reduces the number of expensive objective function evaluations needed but has a large computational overhead: choosing where to evaluate requires optimizing a nested Monte Carlo-based acquisition function for each node in the network. To address this, we propose an accelerated p-KGFN algorithm that reduces computational overhead with only a modest loss in query efficiency. Key to our approach is generation of node-specific candidate inputs for each node in the network via one inexpensive global Monte Carlo simulation. Numerical experiments show that our method maintains competitive query efficiency while achieving up to a 16x speedup over the original p-KGFN algorithm.<\/div>\n

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\n Poompol Buathong, Peter I. Frazier
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Fast Bayesian Optimization of Function Networks with Partial Evaluations arXiv:2506.11456v1 Announce Type: new Abstract: Bayesian optimization of function networks (BOFN) is a framework for optimizing expensive-to-evaluate objective functions structured as networks, where some nodes’ outputs serve as inputs for others. Many real-world applications, such as manufacturing and drug discovery, involve function networks with additional properties […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,112],"tags":[598,1315,491],"class_list":["post-4624","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-evaluations","tag-function","tag-networks"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4624"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=4624"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4624\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=4624"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=4624"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=4624"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}