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Variance reduction techniques for stochastic proximal point algorithms
Stochastic proximal point algorithms have been studied as an alternative to stochastic gradient algorithms since they are more stable with respect to the choice of the stepsize but a proper variance reduced version is missing.
Iterative regularization in classification via hinge loss diagonal descent
In this paper, we focus on iterative regularization in the context of classification.
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