Hide&Seek: Learning to Explain in an End-to-End Differentiable Network
Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models. In contrast to global feature selection techniques, instance-wise methods dynamically identify important features for each instance. A growing number of methods learn a selector, which identifies important features, and a predictor, which uses these to make predictions. However, these pioneering methods face challenges including information leakage and lack of differentiability, which can slow training. In this paper, we present Hide&Seek, an end-to-end differentiable model for instance-wise feature selection. We jointly learn feature selection and prediction under a s...
| Authors | Tal Ellinson, Hadi Mohasel Afshar, Sally Cripps |
|---|---|
| Primary category | stat.ML |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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