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.
Convergence rates for deterministic and stochastic subgradient methods without Lipschitz continuity.SIAM Journal on Optimization, 29(2):1350–1365
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Normalized momentum SGD and variance-reduced STORM achieve O(ε^{-6}) and O(ε^{-4}) oracle complexities respectively under quadratic distance-dependent noise in nonconvex stochastic optimization.
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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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Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
Normalized momentum SGD and variance-reduced STORM achieve O(ε^{-6}) and O(ε^{-4}) oracle complexities respectively under quadratic distance-dependent noise in nonconvex stochastic optimization.