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JOURNAL OF INTELLIGENT MANUFACTURING
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Tianyi Chen et al.
Summary: Stochastic compositional optimization generalizes classic stochastic optimization for minimizing compositions of functions, with applications in reinforcement learning and meta learning. The new Stochastically Corrected Stochastic Compositional gradient method (SCSC) ensures convergence at the same rate as traditional methods and can be accelerated with SGD techniques. Applying Adam to SCSC achieves state-of-the-art performance in stochastic compositional optimization, tested in model-agnostic meta-learning tasks.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
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Yipu Xu et al.
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Tao Zhang et al.
CERAMICS INTERNATIONAL
(2019)