Rejection via Learning Density Ratios

• URL: http://arxiv.org/abs/2405.18686v1
• Date: Wed, 29 May 2024 01:32:17 GMT
• Title: Rejection via Learning Density Ratios
• Authors: Alexander Soen, Hisham Husain, Philip Schulz, Vu Nguyen,
• Abstract summary: Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
• Score: 50.91522897152437
• License: http://creativecommons.org/licenses/by/4.0/
• 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 $ \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.

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