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arXiv · 2002.03755

Compositional ADAM: An Adaptive Compositional Solver

Rasul Tutunov·Minne Li·Alexander I. Cowen-Rivers·Jun Wang·Haitham Bou-Ammar
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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.

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Rasul Tutunov, Minne Li, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou-Ammar. 2020-02-10. Compositional ADAM: An Adaptive Compositional Solver. https://arxiv.org/abs/2002.03755

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