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Model selection on validation data is an essential step in machine learning.While the mixing of data between training and validation is considered taboo,practitioners often violate it to increase performance. Here, we offer asimple, practical method for using the validation set for training, whichallows for a continuous, controlled trade-off between performance andoverfitting of model selection. We define the notion ofon-average-validation-stable algorithms as one in which using small portions ofvalidation data for training does not overfit the model selection process. Wethen prove that stable algorithms are also validation stable. Finally, wedemonstrate our method on the MNIST and CIFAR-10 datasets using stablealgorithms as well as state-of-the-art neural networks. Our results showsignificant increase in test performance with a minor trade-off in biasadmitted to the model selection process.

Train on Validation: Squeezing the Data Lemon