Rejection via Learning Density Ratios
<|reference_start|>Rejection via Learning Density Ratios: 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.<|reference_end|>
@article{soen2024rejection,
    title={Rejection via Learning Density Ratios},
    author={Alexander Soen, Hisham Husain, Philip Schulz, Vu Nguyen},
    journal={arXiv preprint arXiv:2405.18686},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18686},
    primaryClass={stat.ML cs.LG}
}