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arXiv · 2305.16905

Improving Neural Additive Models with Bayesian Principles

Kouroche Bouchiat·Alexander Immer·Hugo Yèche·Gunnar Rätsch·Vincent Fortuin
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Abstract

Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks. However, they lack inherent mechanisms that provide calibrated uncertainties and enable selection of relevant features and interactions. Approaching NAMs from a Bayesian perspective, we augment them in three primary ways, namely by a) providing credible intervals for the individual additive sub-networks; b) estimating the marginal likelihood to perform an implicit selection of features via an empirical Bayes procedure; and c) facilitating the ranking of feature pairs as candidates for second-order interaction in fine-tuned models. In particular, we develop Laplace-approximated NAMs (LA-NAMs), which show improved empirical performance on tabular datasets and challenging real-world medical tasks.

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Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin. 2023-05-26. Improving Neural Additive Models with Bayesian Principles. https://arxiv.org/abs/2305.16905

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