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We study the generalization properties of stochastic gradient methods forlearning with convex loss functions and linearly parameterized functions. Weshow that, in the absence of penalizations or constraints, the stability andapproximation properties of the algorithm can be controlled by tuning eitherthe step-size or the number of passes over the data. In this view, theseparameters can be seen to control a form of implicit regularization. Numericalresults complement the theoretical findings.

Generalization Properties and Implicit Regularization for Multiple Passes SGM