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Tabular datasets are the last "unconquered castle" for deep learning, withtraditional ML methods like Gradient-Boosted Decision Trees still performingstrongly even against recent specialized neural architectures. In this paper,we hypothesize that the key to boosting the performance of neural networks liesin rethinking the joint and simultaneous application of a large set of modernregularization techniques. As a result, we propose regularizing plainMultilayer Perceptron (MLP) networks by searching for the optimalcombination/cocktail of 13 regularization techniques for each dataset using ajoint optimization over the decision on which regularizers to apply and theirsubsidiary hyperparameters. We empirically assess the impact of theseregularization cocktails for MLPs in a large-scale empirical study comprising40 tabular datasets and demonstrate that (i) well-regularized plain MLPssignificantly outperform recent state-of-the-art specialized neural networkarchitectures, and (ii) they even outperform strong traditional ML methods,such as XGBoost.

Well-tuned Simple Nets Excel on Tabular Datasets