Uncertainty-aware Sensitivity Analysis Using Rényi Divergences

Topi Paananen, Michael Andersen, Aki Vehtari

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

2 Sitaatiot (Scopus)
33 Lataukset (Pure)

Abstrakti

For nonlinear supervised learning models, assessing the importance of predictor variables or their interactions is not straightforward because importance can vary in the domain of the variables. Importance can be assessed locally with sensitivity analysis using general methods that rely on the model's predictions or their derivatives. In this work, we extend derivative based sensitivity analysis to a Bayesian setting by differentiating the Rényi divergence of a model's predictive distribution. By utilising the predictive distribution instead of a point prediction, the model uncertainty is taken into account in a principled way. Our empirical results on simulated and real data sets demonstrate accurate and reliable identification of important variables and interaction effects compared to alternative methods.

AlkuperäiskieliEnglanti
OtsikkoProceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence
KustantajaJMLR
Sivut1185-1194
TilaJulkaistu - 12 jouluk. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaConference on Uncertainty in Artificial Intelligence - Virtual, Online
Kesto: 27 heinäk. 202129 heinäk. 2021
https://auai.org/uai2021/

Julkaisusarja

NimiProceedings of Machine Learning Research
KustantajaPMLR
Vuosikerta161
ISSN (elektroninen)2640-3498

Conference

ConferenceConference on Uncertainty in Artificial Intelligence
LyhennettäUAI
KaupunkiVirtual, Online
Ajanjakso27/07/202129/07/2021
www-osoite

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