  Deep neural networks have revolutionized many fields, but their black-box
nature also occasionally prevents their wider adoption in fields such as
healthcare and finance, where interpretable and explainable models are
required. The recent development of Neural Additive Models (NAMs) is a
significant step in the direction of interpretable deep learning for tabular
datasets. In this paper, we propose a new subclass of NAMs that use a
single-layer neural network construction of the Gaussian process via random
Fourier features, which we call Gaussian Process Neural Additive Models
(GP-NAM). GP-NAMs have the advantage of a convex objective function and number
of trainable parameters that grows linearly with feature dimensionality. It
suffers no loss in performance compared to deeper NAM approaches because GPs
are well-suited for learning complex non-parametric univariate functions. We
demonstrate the performance of GP-NAM on several tabular datasets, showing that
it achieves comparable or better performance in both classification and
regression tasks with a large reduction in the number of parameters.
