A unified recursion framework for stochastic variance-reduced estimation yields high-probability bounds and the first Õ(ε^{-3}) oracle complexity for stochastic optimization with expectation constraints.
Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization.Mathematical Programming, 155(1):267–305
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MCCO combines multistage stochastic programming and conditional stochastic optimization, solved via new multilevel Monte Carlo techniques with polynomial scenario complexity.
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Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation
A unified recursion framework for stochastic variance-reduced estimation yields high-probability bounds and the first Õ(ε^{-3}) oracle complexity for stochastic optimization with expectation constraints.
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Multistage Conditional Compositional Optimization
MCCO combines multistage stochastic programming and conditional stochastic optimization, solved via new multilevel Monte Carlo techniques with polynomial scenario complexity.