Improving Neural Additive Models with Bayesian Principles

Autor:	Bouchiat, Kouroche, Immer, Alexander, Yèche, Hugo, Rätsch, Gunnar, Fortuin, Vincent
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 41st International Conference on Machine Learning (ICML 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.16905 Zobrazit plný text záznamu View this record from Arxiv