@inproceedings{
soen2024rejection,
title={Rejection via Learning Density Ratios},
author={Alexander Soen and Hisham Husain and Philip Schulz and Vu Nguyen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JzcIKnnOpJ}
}
Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. 
The predominant approach is to alter the supervised learning pipeline by augmenting typical loss functions, letting model rejection incur a lower loss than an incorrect prediction.
Instead, we propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance.
This can be formalized via the optimization of a loss's risk with a $ \phi$-divergence regularization term.
Through this idealized distribution, a rejection decision can be made by utilizing the density ratio between this distribution and the data distribution.
We focus on the setting where our $ \phi $-divergences are specified by the family of $ \alpha $-divergence.
Our framework is tested empirically over clean and noisy datasets.
Rejection via Learning Density Ratios
Alexander Soen Hisham Husain Philip Schulz Vu Nguyen