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Life sciences · Preprint
arXiv · August 17, 2026
The material analysed did not support any firm read.
This is a preprint describing a computational algorithm (EMS Coreset) designed to improve efficiency of Optimal Transport-based coreset selection through closed-form updates and non-uniform weighting. The work is purely methodological, establishing theoretical consistency and stability properties, and demonstrates runtime improvements on synthetic and real-world benchmarks. It has no clinical validation or application to patient care.
Preprint.
Proposed method achieves competitive or improved approximation quality while substantially reducing runtime compared to Wasserstein- and standard Sinkhorn-based coreset selection, especially at large scale Asymptotic consistency of selected measure and Lipschitz stability to data perturbations established
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The source did not state who this applies to in practice.
This is a methodological contribution presenting a novel algorithm for computational efficiency in coreset selection; it lacks clinical outcome data, patient populations, or validation against established clinical benchmarks.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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Coresets distill large datasets into small, representative subsets for efficient downstream learning. Yet Optimal Transport (OT)-based selection typically requires intensive computation of transport plans, limiting scalability. We introduce a scalable Sinkhorn coreset method that permits closed-form updates of the entropically regularized OT coupling by allowing non-uniform coreset weights. This produces centroids that generalize k-means via soft assignments. We establish asymptotic consistency of the selected measure and Lipschitz stability to data perturbations, providing accuracy and robustness guarantees. Across synthetic and real-world benchmarks, the proposed method achieves competitive or improved approximation quality while substantially reducing runtime compared to Wasserstein- and standard Sinkhorn-based coreset selection, especially at large scale.
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