# Debiasing Stochastic Gradient Descent to handle missing values

@article{Sportisse2020DebiasingSG, title={Debiasing Stochastic Gradient Descent to handle missing values}, author={Aude Sportisse and Claire Boyer and Aymeric Dieuleveut and Julie Josse}, journal={arXiv: Statistics Theory}, year={2020} }

A major caveat of large scale data is their incom-pleteness. We propose an averaged stochastic gradient algorithm handling missing values in linear models. This approach has the merit to be free from the need of any data distribution modeling and to account for heterogeneous missing proportion. In both streaming and finite-sample settings, we prove that this algorithm achieves convergence rate of O(1 n) at the iteration n, the same as without missing values. We show the convergence behavior and… CONTINUE READING

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