Squirrel: A Switching Hyperparameter Optimizer
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Abstract (arXiv)
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge. Motivated by the fact that different optimizers work well on different problems, our approach switches between different optimizers. Since the team names on the competition's leaderboard were randomly generated "alliteration nicknames", consisting of an adjective and an animal with the same initial letter, we called our approach the Switching Squirrel, or here, short, Squirrel.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Dec 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1.
4 papers.
- 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
- 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 146
- 2022Hybrid quantum ResNet for car classification and its hyperparameter optimizationcited by 17
- 2024Evolutionary Retrofittingcited by 2
- 2024Evolutionary Retrofitting15 Oct 2024
- 2022Hybrid quantum ResNet for car classification and its hyperparameter optimization10 May 2022
- 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization20 Sep 2021
- 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 202020 Apr 2021