Abstract
Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics. Expert knowledge elicitation can help, and a strong line of work focuses on directly eliciting informative prior distributions for parameters. This either requires considerable statistical expertise or is laborious, as the emphasis has been on accuracy and not on efficiency of the process. Another line of work queries about importance of features one at a time, assuming them to be independent and hence missing covariance information. In contrast, we propose eliciting expert knowledge about pairwise feature similarities, to borrow statistical strength in the predictions, and using sequential decision making techniques to minimize the effort of the expert. Empirical results demonstrate improvement in predictive performance on both simulated and real data, in high-dimensional linear regression tasks, where we learn the covariance structure with a Gaussian process, based on sequential elicitation.
Original language | English |
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Title of host publication | Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 |
Pages | 1959-1966 |
Number of pages | 8 |
ISBN (Electronic) | 978-0-9992411-4-1 |
DOIs | |
Publication status | Published - Jul 2019 |
MoE publication type | A4 Article in a conference publication |
Event | International Joint Conference on Artificial Intelligence - Venetian Macao Resort Hotel, Macao, China Duration: 10 Aug 2019 → 16 Aug 2019 Conference number: 28 https://ijcai19.org/ http://ijcai19.org/ |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Publisher | International Joint Conferences on Artificial Intelligence |
ISSN (Print) | 1045-0823 |
Conference
Conference | International Joint Conference on Artificial Intelligence |
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Abbreviated title | IJCAI |
Country | China |
City | Macao |
Period | 10/08/2019 → 16/08/2019 |
Internet address |
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