🤖 AI Summary
This work addresses the problem that classification models are forced to predict even under high uncertainty. We propose a rejection mechanism based on density ratio estimation (DRE), modeling rejection as estimating the ratio between the true data distribution and an idealized target distribution. Our method optimizes a risk function regularized by α-divergence—departing for the first time from conventional loss-augmentation paradigms. It endows rejection decisions with well-defined probabilistic semantics and distribution-level interpretability, and naturally accommodates pre-trained models. Empirically, it significantly improves both rejection accuracy and classification reliability on both clean and noisy datasets. Key contributions are: (1) the first formalization of rejection learning as a DRE problem; (2) the introduction of φ-divergence regularization—specifically the α-divergence family—to achieve distributionally robust optimization; and (3) a theoretically rigorous yet practical framework for interpretable rejection.
📝 Abstract
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 $varphi$-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 $varphi$-divergences are specified by the family of $alpha$-divergence. Our framework is tested empirically over clean and noisy datasets.