---
title: "Bayesian Optimization in High Dimensions via Random Embeddings"
authors:
  - "Ziyu Wang"
  - "Masrour Zoghi"
  - "Frank Hutter"
  - "David Matheson"
  - "Nando de Freitas"
doi: ""
url: "https://ijcai.org/proceedings/2013/263"
pdf: "https://ijcai.org/proceedings/2013/0263.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1778-1783"
track: ""
keywords:
---

# Bayesian Optimization in High Dimensions via Random Embeddings

Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando de Freitas

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 1778-1783.

## Abstract

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.