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.
SIAM, 1999
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Gamma-convergence of bending energy to the Sadowsky functional holds for geometrically frustrated anisotropic ribbons with curved references under affine boundary conditions.
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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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On the Sadowsky functional for anisotropic ribbons
Gamma-convergence of bending energy to the Sadowsky functional holds for geometrically frustrated anisotropic ribbons with curved references under affine boundary conditions.