Bayesian Optimization of Combinatorial Structures

Ricardo Baptista, Matthias Poloczek
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:462-471, 2018.

Abstract

The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine learning, and critically require new algorithmic ideas. This article proposes, to the best of our knowledge, the first algorithm to overcome these challenges, based on an adaptive, scalable model that identifies useful combinatorial structure even when data is scarce. Our acquisition function pioneers the use of semidefinite programming to achieve efficiency and scalability. Experimental evaluations demonstrate that this algorithm consistently outperforms other methods from combinatorial and Bayesian optimization.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-baptista18a, title = {{B}ayesian Optimization of Combinatorial Structures}, author = {Baptista, Ricardo and Poloczek, Matthias}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {462--471}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/baptista18a/baptista18a.pdf}, url = {https://proceedings.mlr.press/v80/baptista18a.html}, abstract = {The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine learning, and critically require new algorithmic ideas. This article proposes, to the best of our knowledge, the first algorithm to overcome these challenges, based on an adaptive, scalable model that identifies useful combinatorial structure even when data is scarce. Our acquisition function pioneers the use of semidefinite programming to achieve efficiency and scalability. Experimental evaluations demonstrate that this algorithm consistently outperforms other methods from combinatorial and Bayesian optimization.} }
Endnote
%0 Conference Paper %T Bayesian Optimization of Combinatorial Structures %A Ricardo Baptista %A Matthias Poloczek %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-baptista18a %I PMLR %P 462--471 %U https://proceedings.mlr.press/v80/baptista18a.html %V 80 %X The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine learning, and critically require new algorithmic ideas. This article proposes, to the best of our knowledge, the first algorithm to overcome these challenges, based on an adaptive, scalable model that identifies useful combinatorial structure even when data is scarce. Our acquisition function pioneers the use of semidefinite programming to achieve efficiency and scalability. Experimental evaluations demonstrate that this algorithm consistently outperforms other methods from combinatorial and Bayesian optimization.
APA
Baptista, R. & Poloczek, M.. (2018). Bayesian Optimization of Combinatorial Structures. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:462-471 Available from https://proceedings.mlr.press/v80/baptista18a.html.

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