Towards Robust and Automatic Hyper-Parameter Tunning

• URL: http://arxiv.org/abs/2111.14056v1
• Date: Sun, 28 Nov 2021 05:27:34 GMT
• Title: Towards Robust and Automatic Hyper-Parameter Tunning
• Authors: Mathieu Tuli and Mahdi S. Hosseini and Konstantinos N. Plataniotis
• Abstract summary: We introduce a new class of HPO method and explore how the low-rank factorization of intermediate layers of a convolutional network can be used to define an analytical response surface. We quantify how this surface behaves as a surrogate to model performance and can be solved using a trust-region search algorithm, which we call autoHyper.
• Score: 39.04604349338802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of hyper-parameter optimization (HPO) is burdened with heavy computational costs due to the intractability of optimizing both a model's weights and its hyper-parameters simultaneously. In this work, we introduce a new class of HPO method and explore how the low-rank factorization of the convolutional weights of intermediate layers of a convolutional neural network can be used to define an analytical response surface for optimizing hyper-parameters, using only training data. We quantify how this surface behaves as a surrogate to model performance and can be solved using a trust-region search algorithm, which we call autoHyper. The algorithm outperforms state-of-the-art such as Bayesian Optimization and generalizes across model, optimizer, and dataset selection. The PyTorch codes can be found in \url{https://github.com/MathieuTuli/autoHyper}.

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