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Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite, number of loss functions.
Quasi-martingales
D. L. Fisk · 1965
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Riemannian center of mass and mollifier smoothing
H Karcher · 1977
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An Introduction to the Geometry of Alexandrov Spaces
K. Shiohama · 1993
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Eigentaste: A constant time collaborative filtering algorithm
K. Goldberg, T. Roeder, D. Gupta, and C. Perkins · 2001
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Optimization Algorithms on Matrix Manifolds
P.-A. Absil, R. Mahony, and R. Sepulchre · 2008
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Online identification and tracking of subspaces from highly incomplete information
L. Balzano, R. Nowak, and B. Recht · 2010
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A stochastic gradient method with an exponential convergence rate for finite training sets
N. L. Roux, M. Schmidt, and F. R. Bach · 2012
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Proximal stochastic dual coordinate ascent
S. Shalev-Shwartz and T. Zhang · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
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Stochastic dual coordinate ascent methods for regularized loss minimization
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Incremental majorization-minimization optimization with application to largescale machine learning
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Fast and simple PCA via convex optimization
D. Garber and E. Hazan · 2015
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S. Shalev-Shwartz · 2015
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Improved SVRG for non-strongly-convex or sum-of-non-convex objectives
Z. Allen-Zhu and Y. Yan · 2015
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Fast stochastic algorithms for SVD and PCA: Convergence properties and convexity
O. Shamir · 2015
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Y. Zhang and L Xiao · 2014
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B. Mishra and R. Sepulchre · 2014
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Manopt: a Matlab toolbox for optimization on manifolds
N. Boumal, B. Mishra, P.-A. Absil, and R. Sepulchre · 2014
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Low-rank tensor completion by Riemannian optimization
D. Kressner, M. Steinlechner, and B. Vandereycken · 2014
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Kernel methods on riemannian manifolds with gaussian rbf kernels
S. Jayasumana, R. Hartley, M. Salzmann, H. Li, and M. Harandi · 2015
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Variance reduction for faster non-convex optimization
Z. Allen-Zhu and E. Hazan · 2016
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First-order methods for geodesically convex optimization
H. Zhang and S. Sra · 2016
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