Reducing the variance in online optimization by transporting past gradients

NeurIPS 2019 Sébastien M. R. ArnoldPierre-Antoine ManzagolReza BabanezhadIoannis MitliagkasNicolas Le Roux

Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing past gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online setting... (read more)

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