UAI 2021poster48 citations
Learnable uncertainty under Laplace approximations
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Abstract
Laplace approximations are classic, computationally lightweight means for constructing Bayesian neural networks (BNNs). As in other approximate BNNs, one cannot necessarily expect the induced predictive uncertainty to be calibrated. Here we develop a formalism to explicitly “train” the uncertainty in a decoupled way to the prediction itself. To this end, we introduce
BibTeX
@InProceedings{pmlr-v161-kristiadi21a,
title = {Learnable uncertainty under Laplace approximations},
author = {Kristiadi, Agustinus and Hein, Matthias and Hennig, Philipp},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
pages = {344--353},
year = {2021},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
volume = {161},
series = {Proceedings of Machine Learning Research},
month = {27--30 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v161/kristiadi21a/kristiadi21a.pdf},
url = {https://proceedings.mlr.press/v161/kristiadi21a.html},
abstract = {Laplace approximations are classic, computationally lightweight means for constructing Bayesian neural networks (BNNs). As in other approximate BNNs, one cannot necessarily expect the induced predictive uncertainty to be calibrated. Here we develop a formalism to explicitly “train” the uncertainty in a decoupled way to the prediction itself. To this end, we introduce