@inproceedings{
nguyen2024combining,
title={Combining Statistical Depth and Fermat Distance for Uncertainty Quantification},
author={Hai-Vy Nguyen and Fabrice Gamboa and Reda Chhaibi and Sixin Zhang and Serge Gratton and Thierry Giaccone},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=xeXRhTUmcf}
}
We measure the out-of-domain uncertainty in the prediction of Neural Networks using a statistical notion called "Lens Depth'' (LD) combined with Fermat Distance, which is able to capture precisely the "depth'' of a point with respect to a distribution in feature space, without any distributional assumption. Our method also has no trainable parameter. The method is applied directly in the feature space at test time and does not intervene in training process. As such, it does not impact the performance of the original model. The proposed method gives excellent qualitative results on toy datasets and can give competitive or better uncertainty estimation on standard deep learning datasets compared to strong baseline methods.
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
Hai-Vy Nguyen Fabrice Gamboa Reda Chhaibi Sixin Zhang Serge Gratton Thierry Giaccone