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Recently it has been shown that the step sizes of a family of variance reduced gradient methods called the JacSketch methods depend on the expected smoothness constant.
A stochastic approximation method
Robbins, H. and Monro, S · 1951
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Probability inequalities for sums of bounded random variables
Hoeffding, W · 1963
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Note on sampling without replacing from a finite collection of matrices
Gross, D. and Nesme, V · 2010
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Libsvm: a library for support vector machines
Chang, C.-C. and Lin, C.-J · 2011
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Improved analysis of the subsampled randomized Hadamard transform
Tropp, J. A · 2011
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User-friendly tail bounds for sums of random matrices
Tropp, J. A · 2012
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Sharp analysis of low-rank kernel matrix approximations
Bach, F · 2013
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Introductory Lectures on Convex Optimization: A Basic Course
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SARAH: A novel method for machine learning problems using stochastic recursive gradient
Nguyen, L. M., Liu, J., Scheinberg, K., and Takáč, M · 2017
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Minimizing finite sums with the stochastic average gradient
Schmidt, M., Le Roux, N., and Bach, F · 2017
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Stochastic quasi-gradient methods: Variance reduction via Jacobian sketching
Gower, R. M., Richtárik, P., and Bach, F · 2018
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Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
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Raj, A. and Stich, S. U · 2018
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