Papers › Rejection via Learning Density Ratios
Rejection via Learning Density Ratios
Alexander Soen, Hisham Husain, Philip Schulz, Vu Nguyen
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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