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Stochastic composition optimization draws much attention recently and has been successful in many emerging applications of machine learning, statistical analysis, and reinforcement learning.
A method for unconstrained convex minimization problem with the rate of convergence o (1/k2)
Y. Nesterov · 1983
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Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
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Introductory lectures on convex optimization: A basic course
Y. Nesterov · 2013
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Policy evaluation with temporal differences: a survey and comparison
C. Dann, G. Neumann, and J. Peters · 2014
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M. Wang, E. X. Fang, and H. Liu · 2014
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A proximal stochastic gradient method with progressive variance reduction
L. Xiao and T. Zhang · 2014
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Optimization methods for large-scale machine learning
L. Bottou, F. E. Curtis, and J. Nocedal · 2016
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Learning from conditional distributions via dual kernel embeddings
B. Dai, N. He, Y. Pan, B. Boots, and L. Song · 2016
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Statistical estimation of composite risk functionals and risk optimization problems
D. Dentcheva, S. Penev, and A. Ruszczyński · 2016
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Zeroth-order asynchronous doubly stochastic algorithm with variance reduction
B. Gu, Z. Huo, and H. Huang · 2016
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Finite-sum composition optimization via variance reduced gradient descent
X. Lian, M. Wang, and J. Liu · 2016
Fast stochastic methods for nonsmooth nonconvex optimization
S. J. Reddi, S. Sra, B. Poczos, and A. Smola · 2016
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Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization
S. J. Reddi, S. Sra, B. Poczos, and A. J. Smola · 2016
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Accelerating stochastic composition optimization
M. Wang and J. Liu · 2016
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Asynchronous mini-batch gradient descent with variance reduction for non-convex optimization
Z. Huo and H. Huang · 2017
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Fast stochastic variance reduced admm for stochastic composition optimization
Y. Yu and L. Huang · 2017
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Cited alongside, same era.