Hide&Seek: Learning to Explain in an End-to-End Differentiable Network

Abstract: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 single objective without information leakage. Hide&Seek outperforms existing state-of-the-art models across a range of experiments and is fast to train. We achieve this by reformulating feature removal as a differentiable operation where instead of discretely removing features, we replace a proportion of each feature. Training is further stabilized via a parsimony-weight annealing framework.

27 pages, 12 figures, 16 tables. Accepted at ICML 2026 (PMLR 306). Code at this https URL

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2608.16689 [stat.ML]

(or arXiv:2608.16689v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2608.16689 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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