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Stabilizing Bi-Level Hyperparameter Optimization using Moreau-Yosida Regularization

Machine Learning 2020-07-28 v1 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2007.13322,
  title  = {Stabilizing Bi-Level Hyperparameter Optimization using Moreau-Yosida Regularization},
  author = {Sauptik Dhar and Unmesh Kurup and Mohak Shah},
  journal= {arXiv preprint arXiv:2007.13322},
  year   = {2020}
}

Comments

AutoML, Hyperparameter Optimization (HPO), Bi-Level Optimization, Alternating Direction Method of Multipliers (ADMM)

R2 v1 2026-06-23T17:25:15.360Z