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On the number of modes of Gaussian kernel density estimators

Statistics Theory 2025-11-10 v3 Machine Learning Statistics Theory

Abstract

We consider the Gaussian kernel density estimator with bandwidth β12\beta^{-\frac12} of nn iid Gaussian samples. Using the Kac-Rice formula and an Edgeworth expansion, we prove that the expected number of modes on the real line scales as Θ(βlogβ)\Theta(\sqrt{\beta\log\beta}) as β,n\beta,n\to\infty provided ncβn2cn^c\lesssim \beta\lesssim n^{2-c} for some constant c>0c>0. An impetus behind this investigation is to determine the number of clusters to which Transformers are drawn in a metastable state.

Keywords

Cite

@article{arxiv.2412.09080,
  title  = {On the number of modes of Gaussian kernel density estimators},
  author = {Borjan Geshkovski and Philippe Rigollet and Yihang Sun},
  journal= {arXiv preprint arXiv:2412.09080},
  year   = {2025}
}