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Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems

arXiv — cs.LG•Monday, November 3, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems

A new method for hyperparameter optimization on high-performance computing systems has been introduced, which allows for the evaluation of multiple configurations in parallel. This approach, based on successive halving and a bandit-based strategy, aims to enhance the efficiency of the optimization process, making it faster and more effective. This is significant as it can lead to improved performance in machine learning models, especially when dealing with large datasets, ultimately benefiting various industries relying on advanced data analysis.
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