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Solving Finite-sum Coupled Compositional Optimization via Multi-block-Single-probe Estimator
Traditional variance reduction methods (e.g., SPIDER, SARAH, STORM) have been extensively investigated for improving the convergence rates of stochastic optimization. These techniques typically maintain a sequence of estimators for a single function (or gradient) across iterations. However, what if
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