Improving Neural Network Generalization on Data-Limited Regression with Doubly-Robust Boosting
Hao Wang
Abstract: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.… Show more
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