GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
This addresses the high financial and environmental costs of training large models for machine learning practitioners, though it is an incremental improvement in data selection methods.
The paper tackles the problem of reducing computational costs in deep model training by proposing GRAD-MATCH, a framework that selects data subsets matching the gradient of the full dataset, achieving the best accuracy-efficiency trade-off and outperforming other data-selection algorithms.
The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs. One way to address this is by extracting subsets that generalize on par with the full data. In this work, we propose a general framework, GRAD-MATCH, which finds subsets that closely match the gradient of the training or validation set. We find such subsets effectively using an orthogonal matching pursuit algorithm. We show rigorous theoretical and convergence guarantees of the proposed algorithm and, through our extensive experiments on real-world datasets, show the effectiveness of our proposed framework. We show that GRAD-MATCH significantly and consistently outperforms several recent data-selection algorithms and achieves the best accuracy-efficiency trade-off. GRAD-MATCH is available as a part of the CORDS toolkit: \url{https://github.com/decile-team/cords}.