Author

Satyaki Mukherjee

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#machine learning Preprint Sep 2026

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

This work shows that significantly smaller coresets are possible under mild and natural assumptions on the underlying data distribution, and introduces a new correlated sampling framework, called \textit{determinantal sampling}, based on a novel application of determinantal point processes.

Diptarka Chakraborty, Satyaki Mukherjee, Gaurav Vallabhdas Revankar et al. · 0 citations