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：神经加性模型（NAMs）通过将输入特征分别置于独立的加性子网络中处理，增强了深度神经网络的透明度。然而，此类模型缺乏内在机制来提供校准后的不确定性估计，并无法实现相关特征及其交互作用的有效选择。我们从贝叶斯视角出发，以三种主要方式对NAMs进行增强，具体包括：a) 为各个加性子网络提供可信区间；b) 通过经验贝叶斯过程估计边际似然，实现特征隐式选择；c) 促进特征对排序以作为微调模型中二阶交互的候选。特别地，我们提出了拉普拉斯近似神经加性模型（LA-NAMs），该模型在表格数据集和具有挑战性的现实医疗任务中展现出更优的经验性能。