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Output details

11 - Computer Science and Informatics

University of Oxford

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Output 50 of 263 in the submission

Output title

Bayesian Optimization in High Dimensions via Random Embeddings

Type

E - Conference contribution

DOI

Name of conference/published proceedings

(IJCAI ‘13) Proceedings of the 23rd International Joint Conference on Artificial Intelligence

Volume number

Issue number

First page of article

1778

ISSN of proceedings

Year of publication

2013

URL

http://ijcai.org/papers13/Papers/IJCAI13-263.pdf

Number of additional authors

Additional information

<24>

Big data organizations typically have millions of users and complex systems implemented by different teams. Automation is essential to deliver personalized products and to configure hardware, software, interfaces and tests. Bayesian optimization is a powerful approach for increased automation in these domains, provided the number of parameters (dimensions) is small. Scaling to high dimensions has been the holy grail of Bayesian optimization for decades, but little progress was made due to the difficulty of the problem. Here, we introduce a novel approach that scales to high dimensions, and provide theoretical analyses. The paper received a prestigious IJCAI Distinguished Paper Award.

Interdisciplinary

Cross-referral requested

Research group

None

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract