Iterate averaging as regularization for stochastic gradient descent

Gergely Neu; Lorenzo Rosasco

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arXiv · 1802.08009

Iterate averaging as regularization for stochastic gradient descent

Gergely Neu·Lorenzo Rosasco

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Abstract

We propose and analyze a variant of the classic Polyak-Ruppert averaging scheme, broadly used in stochastic gradient methods. Rather than a uniform average of the iterates, we consider a weighted average, with weights decaying in a geometric fashion. In the context of linear least squares regression, we show that this averaging scheme has a the same regularizing effect, and indeed is asymptotically equivalent, to ridge regression. In particular, we derive finite-sample bounds for the proposed approach that match the best known results for regularized stochastic gradient methods.

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Gergely Neu, Lorenzo Rosasco. 2018-02-22. Iterate averaging as regularization for stochastic gradient descent. https://arxiv.org/abs/1802.08009

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