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New method advances density estimation for Hellinger distance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for density estimation, extending the minimum-distance estimator approach to Hellinger distance. This technique allows for the creation of near-linear time algorithms with near-optimal sample complexity for learning classes of densities, including univariate mixtures of log-concave densities and mixtures of Gaussians. AI

RANK_REASON This is a research paper detailing a new algorithmic approach to density estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Spencer Compton, Jerry Li ·

    Density estimation for Hellinger via minimum-distance estimators: mixtures of Gaussians, log-concave, and more

    arXiv:2606.11469v1 Announce Type: cross Abstract: We study the task of density estimation, where we hope to accurately estimate a probability density from $n$ samples. A textbook method for density estimation in total variation distance is the minimum-distance estimator approach,…

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