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.

翻译：神经可加模型（NAMs）通过将输入特征分别处理于独立的可加性子网络，从而提升深度神经网络的透明度。然而，这类模型缺乏能够提供校准不确定性、实现相关特征及交互作用选择的内在机制。本文从贝叶斯视角出发，通过三种主要方式对NAMs进行增强：a) 为各可加性子网络提供可信区间；b) 通过经验贝叶斯方法估计边缘似然以执行特征的隐式选择；c) 在微调模型中促进特征对的排序，作为二阶交互作用的候选。特别地，我们提出了拉普拉斯近似神经可加模型（LA-NAMs），该模型在表格数据集和具有挑战性的真实世界医疗任务上展现出更优的实证性能。