Summary of Funbo: Discovering Acquisition Functions For Bayesian Optimization with Funsearch, by Virginia Aglietti et al.
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
by Virginia Aglietti, Ira Ktena, Jessica Schrouff, Eleni Sgouritsa, Francisco J. R. Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, FunBO, leverages Large Language Models (LLMs) to learn novel acquisition functions (AFs) for Bayesian optimization. By using a limited number of evaluations for a set of objective functions, FunBO can be used to learn new AFs written in computer code. The paper provides the analytic expression of all discovered AFs and evaluates them on various global optimization benchmarks and hyperparameter optimization tasks. Results show that FunBO identifies AFs that generalize well in and out of the training distribution of functions, outperforming established general-purpose AFs and achieving competitive performance against customized AFs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FunBO uses Large Language Models to learn new acquisition functions for Bayesian optimization. This means it can figure out how to find the best spot to test next without needing a lot of tries. It does this by looking at a few examples and then using that information to make smart guesses. The paper shows that FunBO works well on different types of problems, even ones it hasn’t seen before. |
Keywords
* Artificial intelligence * Hyperparameter * Optimization
