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• W3005222308 abstract "In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values. We proof that C-ADAM converges to a stationary point in $mathcal{O}(delta^{-2.25})$ with $delta$ being a precision parameter. Moreover, we demonstrate the importance of our results by bridging, for the first time, model-agnostic meta-learning (MAML) and compositional optimisation showing fastest known rates for deep network adaptation to-date. Finally, we validate our findings in a set of experiments from portfolio optimisation and meta-learning. Our results manifest significant sample complexity reductions compared to both standard and compositional solvers." @default.
• W3005222308 created "2020-02-14" @default.
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• W3005222308 date "2020-02-10" @default.
• W3005222308 modified "2023-09-27" @default.
• W3005222308 title "Compositional ADAM: An Adaptive Compositional Solver" @default.
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• W3005222308 hasPublicationYear "2020" @default.
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