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FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch

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arxiv 2406.04824 v2 pith:JALCAYJA submitted 2024-06-07 cs.LG stat.ML

Virginia Aglietti , Ira Ktena , Jessica Schrouff , Eleni Sgouritsa , Francisco J. R. Ruiz , Alan Malek , Alexis Bellot , Silvia Chiappa This is my paper
classification cs.LGstat.ML
keywords optimizationfunctionsfunboacquisitionacrossalgorithmsbayesianevaluations
verification ladder T0 review T1 audit T2 compute T3 formal
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The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations. The best-performing AF can vary significantly across optimization problems, often requiring ad-hoc and problem-specific choices. This work tackles the challenge of designing novel AFs that perform well across a variety of experimental settings. Based on FunSearch, a recent work using Large Language Models (LLMs) for discovery in mathematical sciences, we propose FunBO, an LLM-based method that can be used to learn new AFs written in computer code by leveraging access to a limited number of evaluations for a set of objective functions. We provide the analytic expression of all discovered AFs and evaluate them on various global optimization benchmarks and hyperparameter optimization tasks. We show how FunBO identifies AFs that generalize well in and out of the training distribution of functions, thus outperforming established general-purpose AFs and achieving competitive performance against AFs that are customized to specific function types and are learned via transfer-learning algorithms.

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Cited by 5 Pith papers

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  3. VLMgineer: Vision Language Models as Robotic Toolsmiths

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  4. Explainable AI-assisted Optimization for Feynman Integral Reduction

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    FunSearch discovered a simple priority function for ordering IBP seeding integrals, reducing the number needed for multi-loop Feynman integral reductions by factors up to 3058.

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