Learning Hyperparameters via a Data-Emphasized Variational Objective

• URL: http://arxiv.org/abs/2502.01861v1
• Date: Mon, 03 Feb 2025 22:19:35 GMT
• Title: Learning Hyperparameters via a Data-Emphasized Variational Objective
• Authors: Ethan Harvey, Mikhail Petrov, Michael C. Hughes,
• Abstract summary: grid search is computationally expensive, requires carving out a validation set, and requires users to specify candidate values. We propose an alternative: directly learning regularization hyperparameters on the full training set via the evidence lower bound ("ELBo") objective. We show how our method reduces the 88+ hour grid search of past work to under 3 hours while delivering comparable accuracy.
• Score: 4.453137996095194
• License:
• Abstract: When training large flexible models, practitioners often rely on grid search to select hyperparameters that control over-fitting. This grid search has several disadvantages: the search is computationally expensive, requires carving out a validation set that reduces the available data for training, and requires users to specify candidate values. In this paper, we propose an alternative: directly learning regularization hyperparameters on the full training set via the evidence lower bound ("ELBo") objective from variational methods. For deep neural networks with millions of parameters, we recommend a modified ELBo that upweights the influence of the data likelihood relative to the prior. Our proposed technique overcomes all three disadvantages of grid search. In a case study on transfer learning of image classifiers, we show how our method reduces the 88+ hour grid search of past work to under 3 hours while delivering comparable accuracy. We further demonstrate how our approach enables efficient yet accurate approximations of Gaussian processes with learnable length-scale kernels.

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