Variance reduction techniques for stochastic proximal point algorithms

Cites Work

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Minimizing finite sums with the stochastic average gradient
Incremental proximal methods for large scale convex optimization
From error bounds to the complexity of first-order descent methods for convex functions
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On damped second-order gradient systems
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Large-Scale Machine Learning with Stochastic Gradient Descent
Nonasymptotic convergence of stochastic proximal point algorithms for constrained convex optimization
The Proximal Robbins–Monro Method
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The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems
Understanding Machine Learning
Some methods of speeding up the convergence of iteration methods
A Stochastic Approximation Method
Convex analysis and monotone operator theory in Hilbert spaces
A semismooth Newton stochastic proximal point algorithm with variance reduction

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