Rejection via Learning Density Ratios

Alexander Soen; Hisham Husain; Philip Schulz; Vu Nguyen

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arXiv · 2405.18686

Rejection via Learning Density Ratios

Alexander Soen·Hisham Husain·Philip Schulz·Vu Nguyen

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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 $φ$-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 $φ$-divergences are specified by the family of $α$-divergence. Our framework is tested empirically over clean and noisy datasets.

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Alexander Soen, Hisham Husain, Philip Schulz, Vu Nguyen. 2025-05-08. Rejection via Learning Density Ratios. https://arxiv.org/abs/2405.18686

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