High dimensional, tabular deep learning with an auxiliary knowledge graph

Machine learning models exhibit strong performance on datasets with abundant labeled samples. Here, our key insight is that there is often abundant, auxiliary domain information describing input features which can be structured as a heterogeneous knowledge graph (KG). We propose PLATO, a method that achieves strong performance on tabular data with d \gg n by using an auxiliary KG describing input features to regularize a multilayer perceptron (MLP). PLATO is based on the inductive bias that two input features corresponding to similar nodes in the auxiliary KG should have similar weight vectors in the MLP's first layer. Across 6 d \gg n datasets, PLATO outperforms 13 state-of-the-art baselines by up to 10.19%.