Compositional ADAM: An Adaptive Compositional Solver

• URL: http://arxiv.org/abs/2002.03755v2
• Date: Fri, 24 Apr 2020 15:10:15 GMT
• Title: Compositional ADAM: An Adaptive Compositional Solver
• Authors: Rasul Tutunov and Minne Li and Alexander I. Cowen-Rivers and Jun Wang and Haitham Bou-Ammar
• Abstract summary: C-ADAM is the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values. We prove that C-ADAM converges to a stationary point in $mathcalO(delta-2.25)$ with $delta$ being a precision parameter.
• Score: 69.31447856853833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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