Density estimation for Hellinger via minimum-distance estimators: mixtures of Gaussians, log-concave, and more
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, where we conclude both the algorithm and the analysis merely from bounding the VC dimension of a particular concept class (the so-called Yatracos class). While this technique has originally yielded sharp guarantees primarily for total variation distance, in this work we extend the minimum-distance estimator approach for learning within Hellinger distance. Our main observation is that we may produce an analogous recipe for Hellinger (where we only require boundin...
| Authors | Spencer Compton, Jerry Li |
|---|---|
| Primary category | cs.DS |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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