Well-tuned Simple Nets Excel on Tabular Datasets (2106.11189v2)

Abstract: Tabular datasets are the last "unconquered castle" for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent specialized neural architectures. In this paper, we hypothesize that the key to boosting the performance of neural networks lies in rethinking the joint and simultaneous application of a large set of modern regularization techniques. As a result, we propose regularizing plain Multilayer Perceptron (MLP) networks by searching for the optimal combination/cocktail of 13 regularization techniques for each dataset using a joint optimization over the decision on which regularizers to apply and their subsidiary hyperparameters. We empirically assess the impact of these regularization cocktails for MLPs in a large-scale empirical study comprising 40 tabular datasets and demonstrate that (i) well-regularized plain MLPs significantly outperform recent state-of-the-art specialized neural network architectures, and (ii) they even outperform strong traditional ML methods, such as XGBoost.

• The paper demonstrates that applying a comprehensive regularization cocktail to MLPs significantly improves performance on tabular datasets.
• The authors optimize 13 modern regularization techniques jointly with hyperparameters to enhance deep learning for structured data.
• Experiments on 40 datasets validate that well-regularized simple nets can surpass traditional methods in domains like finance and healthcare.

Overview of "Well-tuned Simple Nets Excel on Tabular Datasets"

The paper "Well-tuned Simple Nets Excel on Tabular Datasets" presents an incisive paper on the application of deep learning to tabular datasets, a domain traditionally dominated by machine learning approaches such as Gradient-Boosted Decision Trees (GBDTs). This paper provides a meticulous examination of the potential for deep learning models, specifically Multilayer Perceptrons (MLPs), to outperform these traditional methods when appropriately regularized.

Introduction

The authors identify a notable lag in the adoption of deep learning techniques for tabular data, which contrasts with their widespread success in fields dominated by raw data like images and text. They recognize the superior performance of GBDT on structured data. Recent developments in specialized neural architectures, though promising, require careful benchmarking and still send mixed signals regarding their competitiveness against GBDTs.

Methodology

The central hypothesis of the paper is that the application of a comprehensive set of modern regularization techniques can significantly enhance the performance of MLPs on tabular datasets. The authors employ a systematic approach: they construct "regularization cocktails" by selecting an optimal combination of 13 regularization methods tailored to each dataset. This selection is performed through joint optimization targeting both the choice of regularizers and their corresponding hyperparameters.

Key Findings

Through a large-scale empirical paper involving 40 tabular datasets, the authors demonstrate two main findings:

1. Performance Superiority: Regularized MLPs substantially outperform recent specialized neural network architectures and, notably, strong traditional ML methods like XGBoost. This result underscores the potential of well-tuned simple networks to serve as powerful entities for tabular data representation.
2. Significance of Regularization: The paper emphasizes that combining multiple regularization techniques synergistically contributes to this superior performance. This finding spotlights the inadequacy of the typical trial-and-error approach used by practitioners to mix a few regularizers.

Implications

The implications of this research are twofold. Practically, the introduction of regularization cocktails could foster the broader application of deep learning in domains reliant on tabular data, such as finance and healthcare. Theoretically, this challenge encourages further exploration into the specific impacts of various regularization techniques and hyperparameters on model robustness and generalization.

Future Directions

The paper opens several research avenues. First, the integration of meta-learning techniques to expedite the search for optimal regularization combinations could be explored. Second, extending their methodology to regression tasks and diverse tabular data modalities, including highly imbalanced datasets and streaming data, would be valuable. Lastly, coupling this regularization approach with advanced neural architectures could yield further gains.

In conclusion, the paper delivers compelling evidence that with sophisticated regularization, even straightforward neural network architectures can excel on tabular datasets, challenging the hegemony of traditional ML methods and suggesting a promising shift in data representations suited for deep learning.