Book ; Online: Compositional ADAM
An Adaptive Compositional Solver
2020
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 ...
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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Keywords | Computer Science - Machine Learning ; Mathematics - Optimization and Control ; Statistics - Machine Learning |
Publishing date | 2020-02-10 |
Publishing country | us |
Document type | Book ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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