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• W2953884745 abstract "We consider the problem of minimizing the composition of a smooth (nonconvex) function and a smooth vector mapping, where the inner mapping is in the form of an expectation over some random variable or a finite sum. We propose a stochastic composite gradient method that employs an incremental variance-reduced estimator for both the inner vector mapping and its Jacobian. We show that this method achieves the same orders of complexity as the best known first-order methods for minimizing expected-value and finite-sum nonconvex functions, despite the additional outer composition which renders the composite gradient estimator biased. This finding enables a much broader range of applications in machine learning to benefit from the low complexity of incremental variance-reduction methods." @default.
• W2953884745 created "2019-07-12" @default.
• W2953884745 creator A5073029812 @default.
• W2953884745 creator A5081758689 @default.
• W2953884745 date "2019-06-24" @default.
• W2953884745 modified "2023-09-27" @default.
• W2953884745 title "A Stochastic Composite Gradient Method with Incremental Variance Reduction" @default.
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