Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization

Mikita Sazanovich, Anastasiya Nikolskaya, Yury Belousov, Aleksei Shpilman
Proceedings of the NeurIPS 2020 Competition and Demonstration Track, PMLR 133:77-85, 2021.

Abstract

Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results. This paper describes our approach to solving the black-box optimization challenge at NeurIPS 2020 through learning search space partition for local Bayesian optimization. We describe the task of the challenge as well as our algorithm for low budget optimization that we named SPBOpt. We optimize the hyper-parameters of our algorithm for the competition finals using multi-task Bayesian optimization on results from the first two evaluation settings. Our approach has ranked third in the competition finals.

Cite this Paper

BibTeX
@InProceedings{pmlr-v133-sazanovich21a, title = {Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization}, author = {Sazanovich, Mikita and Nikolskaya, Anastasiya and Belousov, Yury and Shpilman, Aleksei}, booktitle = {Proceedings of the NeurIPS 2020 Competition and Demonstration Track}, pages = {77--85}, year = {2021}, editor = {Escalante, Hugo Jair and Hofmann, Katja}, volume = {133}, series = {Proceedings of Machine Learning Research}, month = {06--12 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v133/sazanovich21a/sazanovich21a.pdf}, url = {https://proceedings.mlr.press/v133/sazanovich21a.html}, abstract = {Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results. This paper describes our approach to solving the black-box optimization challenge at NeurIPS 2020 through learning search space partition for local Bayesian optimization. We describe the task of the challenge as well as our algorithm for low budget optimization that we named SPBOpt. We optimize the hyper-parameters of our algorithm for the competition finals using multi-task Bayesian optimization on results from the first two evaluation settings. Our approach has ranked third in the competition finals.} }
Endnote
%0 Conference Paper %T Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization %A Mikita Sazanovich %A Anastasiya Nikolskaya %A Yury Belousov %A Aleksei Shpilman %B Proceedings of the NeurIPS 2020 Competition and Demonstration Track %C Proceedings of Machine Learning Research %D 2021 %E Hugo Jair Escalante %E Katja Hofmann %F pmlr-v133-sazanovich21a %I PMLR %P 77--85 %U https://proceedings.mlr.press/v133/sazanovich21a.html %V 133 %X Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results. This paper describes our approach to solving the black-box optimization challenge at NeurIPS 2020 through learning search space partition for local Bayesian optimization. We describe the task of the challenge as well as our algorithm for low budget optimization that we named SPBOpt. We optimize the hyper-parameters of our algorithm for the competition finals using multi-task Bayesian optimization on results from the first two evaluation settings. Our approach has ranked third in the competition finals.
APA
Sazanovich, M., Nikolskaya, A., Belousov, Y. & Shpilman, A.. (2021). Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization. Proceedings of the NeurIPS 2020 Competition and Demonstration Track, in Proceedings of Machine Learning Research 133:77-85 Available from https://proceedings.mlr.press/v133/sazanovich21a.html.

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