CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization

• URL: http://arxiv.org/abs/2212.06150v1
• Date: Sun, 11 Dec 2022 07:46:19 GMT
• Title: CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization
• Authors: Shuo Yang, Yang Jiao, Shaoyu Dou, Mana Zheng, Chen Zhu
• Abstract summary: CPMLHO is a new hyperparameter optimization method using cutting plane method and mixed-level objective function. Compared to existing methods, our method can automatically update the hyperparameters in the training process.
• Score: 24.39326333982495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The hyperparameter optimization of neural network can be expressed as a bilevel optimization problem. The bilevel optimization is used to automatically update the hyperparameter, and the gradient of the hyperparameter is the approximate gradient based on the best response function. Finding the best response function is very time consuming. In this paper we propose CPMLHO, a new hyperparameter optimization method using cutting plane method and mixed-level objective function.The cutting plane is added to the inner layer to constrain the space of the response function. To obtain more accurate hypergradient,the mixed-level can flexibly adjust the loss function by using the loss of the training set and the verification set. Compared to existing methods, the experimental results show that our method can automatically update the hyperparameters in the training process, and can find more superior hyperparameters with higher accuracy and faster convergence.

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