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Stabilizing Bi-Level Hyperparameter Optimization using Moreau-Yosida Regularization

2020-07-27 · Sauptik Dhar, Unmesh Kurup, Mohak Shah

This research proposes to use the Moreau-Yosida envelope to stabilize the convergence behavior of bi-level Hyperparameter optimization solvers, and introduces the new algorithm called Moreau-Yosida regularized Hyperparameter Optimization (MY-HPO) algorithm. Theoretical analysis on the correctness of the MY-HPO solution and initial convergence analysis is also provided. Our empirical results show significant improvement in loss values for a fixed computation budget, compared to the state-of-art bi-level HPO solvers.

📄 PDF Abstract BibTeX arXiv:2007.13322

Code (1)

lorraine2/hypernet-hypertraining 공식 구현

Tasks

Hyperparameter Optimization

Methods 이 논문이 사용한 방법론

HPO In machine learning, a hyperparameter is a parameter whose value is used to control learning process, and HPO is the problem of choosing a set of optimal hyperparameters for a…

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