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Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling

This story is from 2026-09-09. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.06394v1 Announce Type: new Abstract: Massive datasets in modern machine learning have made data reduction a central challenge, particularly for clustering tasks where memory and computational constraints demand compact yet faithful summaries. A standard approach is to construct an \texti…

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  1. 2026-09-09 04:00 · arXiv stat.ML
    Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling

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