N. de Freitas
- Bayesian Optimization in High Dimensions via Random Embeddings
- 23rd International Joint Conference on Artificial Intelligence
- Book/source title
- IJCAI-13: proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence: Beijing, China, 3-9 August 2013. - Vol. 3
- Pages (from-to)
- Palo Alto, Calif.: AAAI Press/International Joint Conferences on Artificial Intelligence
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple and applies to domains with both categorical and continuous variables. The experiments demonstrate that REMBO can effectively solve high-dimensional problems, including automatic parameter configuration of a popular mixed integer linear programming solver.
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