Parameter Optimization with Conscious Allocation (POCA)

• URL: http://arxiv.org/abs/2312.17404v1
• Date: Fri, 29 Dec 2023 00:13:55 GMT
• Title: Parameter Optimization with Conscious Allocation (POCA)
• Authors: Joshua Inman, Tanmay Khandait, Giulia Pedrielli, and Lalitha Sankar
• Abstract summary: Hyperband-based approaches to machine learning are among the most effective. We present. the new. Optimization with Conscious Allocation (POCA), a hyperband-based algorithm that adaptively allocates the inputted. budget to the hyperparameter configurations it generates. POCA finds strong configurations faster in both settings.
• Score: 4.478575931884855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of modern machine learning algorithms depends upon the selection of a set of hyperparameters. Common examples of hyperparameters are learning rate and the number of layers in a dense neural network. Auto-ML is a branch of optimization that has produced important contributions in this area. Within Auto-ML, hyperband-based approaches, which eliminate poorly-performing configurations after evaluating them at low budgets, are among the most effective. However, the performance of these algorithms strongly depends on how effectively they allocate the computational budget to various hyperparameter configurations. We present the new Parameter Optimization with Conscious Allocation (POCA), a hyperband-based algorithm that adaptively allocates the inputted budget to the hyperparameter configurations it generates following a Bayesian sampling scheme. We compare POCA to its nearest competitor at optimizing the hyperparameters of an artificial toy function and a deep neural network and find that POCA finds strong configurations faster in both settings.

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