# In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors

@inproceedings{Negrea2020InDO, title={In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors}, author={Jeffrey Negrea and Gintare Karolina Dziugaite and Daniel M. Roy}, booktitle={ICML}, year={2020} }

We propose to study the generalization error of a learned predictor $\hat h$ in terms of that of a surrogate (potentially randomized) predictor that is coupled to $\hat h$ and designed to trade empirical risk for control of generalization error. In the case where $\hat h$ interpolates the data, it is interesting to consider theoretical surrogate classifiers that are partially derandomized or rerandomized, e.g., fit to the training data but with modified label noise. We also show that replacing…

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