A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control

Abstract
We consider the problem of unconstrained minimization of a smooth objective function in ℝn in a setting where only function evaluations are possible. While importance sampling is one of the most popular techniques used by machine learning practitioners to accelerate the convergence of their models when applicable, there is not much existing theory for this acceleration in the derivative-free setting. In this paper, we propose the first derivative free optimization method with importance sampling and derive new improved complexity results on non-convex, convex and strongly convex functions. We conduct extensive experiments on various synthetic and real LIBSVM datasets confirming our theoretical results. We test our method on a collection of continuous control tasks on MuJoCo environments with varying difficulty. Experiments show that our algorithm is practical for high dimensional continuous control problems where importance sampling results in a significant sample complexity improvement.

Citation
Bibi, A., Bergou, E. H., Sener, O., Ghanem, B., & Richtarik, P. (2020). A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3275–3282. doi:10.1609/aaai.v34i04.5727

Acknowledgements
This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.

Publisher
Association for the Advancement of Artificial Intelligence (AAAI)

Journal
Proceedings of the AAAI Conference on Artificial Intelligence

DOI
10.1609/aaai.v34i04.5727

arXiv
1902.01272

Additional Links
https://aaai.org/ojs/index.php/AAAI/article/view/5727

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2021-01-21 06:49:11
Published as conference paper
2019-11-27 08:53:53
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2019-05-29 06:31:01
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