NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:3463
Title:Single-Model Uncertainties for Deep Learning

The paper presents an approach for estimating aleatoric uncertainty which leverages the pinball loss in quantile regression, and orthonormal certificates for measuring epistemic uncertainty. Although the pinball loss has been used in prior work, its randomized version for simultaneously optimizing for all quantiles is novel. In addition the novelty of the OC approach for the filtering task is significant. Overall the value of the proposed methods is convincingly demonstrated on a variety of datasets. The reviewers and AC have carefully examined the author feedback and feel that the feedback adequately addresses the concerns raised in the reviewers. We strongly encourage the authors to incorporate their feedback and in particular their new experiments as these would strengthen their submission significantly.