Linearized Laplace Inference in Neural Additive Models
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2023
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Conference Paper
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Abstract
Deep neural networks are highly effective but suffer from a lack of interpretability due to their black-box nature. Neural additive models (NAMs) solve this by separating into additive sub-networks, revealing the interactions between features and predictions. In this paper, we approach the NAM from a Bayesian perspective in order to quantify the uncertainty in the recovered interactions. Linearized Laplace approximation enables inference of these interactions directly in function space and yields a tractable estimate of the marginal likelihood, which can be used to perform implicit feature selection through an empirical Bayes procedure. Empirically, we show that Laplace-approximated NAMs (LA-NAM) are both more robust to noise and easier to interpret than their non-Bayesian counterpart for tabular regression and classification tasks.
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5th Symposium on Advances in Approximate Bayesian Inference (AABI 2023)
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OpenReview
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5th Symposium on Advances in Approximate Bayesian Inference (AABI 2023)
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09568 - Rätsch, Gunnar / Rätsch, Gunnar
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