Variance reduction via empirical variance minimization: convergence and complexity

Belomestny, D., Iosipoi, L., Zhivotovskiy, N.

arXiv.org Machine Learning 

In this paper we propose and study a generic variance reduction approach. The proposed method is based on minimization of the empirical variance over a suitable class of zero mean control functionals. We present the corresponding convergence analysis and analyze complexity. Finally some numerical results showing efficiency of the proposed approach are presented.

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