Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

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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.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019
TilaJulkaistu - heinäkuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Joint Conference on Artificial Intelligence - Macao, Kiina
Kesto: 10 elokuuta 201916 elokuuta 2019
Konferenssinumero: 28
https://ijcai19.org/

Conference

ConferenceInternational Joint Conference on Artificial Intelligence
LyhennettäIJCAI
MaaKiina
KaupunkiMacao
Ajanjakso10/08/201916/08/2019
www-osoite

ID: 33776797