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There is currently a large gap in performance between the statisticallyrigorous methods like linear regression or additive splines and the powerfuldeep methods using neural networks. Previous works attempting to close this gaphave failed to fully investigate the exponentially growing number of featurecombinations which deep networks consider automatically during training. Inthis work, we develop a tractable selection algorithm to efficiently identifythe necessary feature combinations by leveraging techniques in featureinteraction detection. Our proposed Sparse Interaction Additive Networks (SIAN)construct a bridge from these simple and interpretable models to fullyconnected neural networks. SIAN achieves competitive performance againststate-of-the-art methods across multiple large-scale tabular datasets andconsistently finds an optimal tradeoff between the modeling capacity of neuralnetworks and the generalizability of simpler methods.

Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection