# Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses

@article{Bassily2020StabilityOS, title={Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses}, author={Raef Bassily and Vitaly Feldman and Crist'obal Guzm'an and Kunal Talwar}, journal={ArXiv}, year={2020}, volume={abs/2006.06914} }

Uniform stability is a notion of algorithmic stability that bounds the worst case change in the model output by the algorithm when a single data point in the dataset is replaced. An influential work of Hardt et al. (2016) provides strong upper bounds on the uniform stability of the stochastic gradient descent (SGD) algorithm on sufficiently smooth convex losses. These results led to important progress in understanding of the generalization properties of SGD and several applications to…

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