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Variance reduction is a simple and effective technique that accelerates convex (or non-convex) stochastic optimization.
A stochastic approximation method
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A stochastic gradient method with an exponential convergence rate for finite training sets
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Accelerating stochastic gradient descent using predictive variance reduction
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
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Stochastic proximal gradient descent with acceleration techniques
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Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization
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A universal catalyst for first-order optimization
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Limitations on variance-reduction and acceleration schemes for finite sums optimization
Y. Arjevani · 2017
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Less than a single pass: Stochastically controlled stochastic gradient
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Perturbed iterate analysis for asynchronous stochastic optimization
H. Mania, X. Pan, D. Papailiopoulos, B. Recht, K. Ramchandran, and M. I. Jordan · 2017
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Minimizing finite sums with the stochastic average gradient
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Adaptive svrg methods under error bound conditions with unknown growth parameter
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X. Liu and C.-J. Hsieh · 2018
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