Departmental Papers (CIS)

Date of this Version

3-2020

Document Type

Conference Paper

Comments

23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, June 3-5, 2020

Abstract

Reliable uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. However, existing approaches to estimating uncertainty largely ignore the possibility of covariate shift—i.e., where the real-world data distribution may differ from the training distribution. As a consequence, existing algorithms can overestimate certainty, possibly yielding a false sense of confidence in the predictive model. We propose an algorithm for calibrating predictions that accounts for the possibility of covariate shift, given labeled examples from the training distribution and unlabeled examples from the real-world distribution. Our algorithm uses importance weighting to correct for the shift from the training to the real-world distribution. However, importance weighting relies on the training and real-world distributions to be sufficiently close. Building on ideas from domain adaptation, we additionally learn a feature map that tries to equalize these two distributions. In an empirical evaluation, we show that our proposed approach outperforms existing approaches to calibrated prediction when there is covariate shift.

Subject Area

CPS Safe Autonomy

Publication Source

23rd International Conference on Artificial Intelligence and Statistics (AISTATS)

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Date Posted: 03 March 2020

This document has been peer reviewed.