Improving Neural Network Generalization on Data-Limited Regression with Doubly-Robust Boosting

Enhancing the generalization performance of neural networks given limited data availability remains a formidable challenge, due to the model selection trade-off between training error and generalization gap. To handle this challenge, we present a posterior optimization issue, specifically designed to reduce the generalization error of trained neural networks. To operationalize this concept, we propose a Doubly-Robust Boosting machine (DRBoost) which consists of a statistical learner and a zero-order optimizer. The statistical learner reduces the model capacity and thus the generalization gap; the zero-order optimizer minimizes the training error in a gradient-free manner. The two components cooperate to reduce the generalization error of a fully trained neural network in a doubly robust manner. Furthermore, the statistical learner alleviates the multicollinearity in the discriminative layer and enhances the generalization performance. The zero-order optimizer eliminates the reliance on gradient calculation and offers more flexibility in learning objective selection. Experiments demonstrate that DRBoost improves the generalization performance of various prevalent neural network backbones effectively.