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Poster
Provably Faster Algorithms for Bilevel Optimization
Junjie Yang · Kaiyi Ji · Yingbin Liang

Wed Dec 08 04:30 PM -- 06:00 PM (PST) @
in Poster Session 4 »
Bilevel optimization has been widely applied in many important machine learning applications such as hyperparameter optimization and meta-learning. Recently, several momentum-based algorithms have been proposed to solve bilevel optimization problems faster. However, those momentum-based algorithms do not achieve provably better computational complexity than $\mathcal{\widetilde O}(\epsilon^{-2})$ of the SGD-based algorithm. In this paper, we propose two new algorithms for bilevel optimization, where the first algorithm adopts momentum-based recursive iterations, and the second algorithm adopts recursive gradient estimations in nested loops to decrease the variance. We show that both algorithms achieve the complexity of $\mathcal{\widetilde O}(\epsilon^{-1.5})$, which outperforms all existing algorithms by the order of magnitude. Our experiments validate our theoretical results and demonstrate the superior empirical performance of our algorithms in hyperparameter applications.
Keywords: Optimization · Machine Learning · Meta Learning

Author Information

Junjie Yang (Ohio State University)
Kaiyi Ji (The Ohio State University)
Yingbin Liang (The Ohio State University)

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