Some techniques in density estimation
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Abstract (arXiv)
Density estimation is an interdisciplinary topic at the intersection of statistics, theoretical computer science and machine learning. We review some old and new techniques for bounding the sample complexity of estimating densities of continuous distributions, focusing on the class of mixtures of Gaussians and its subclasses. In particular, we review the main techniques used to prove the new sample complexity bounds for mixtures of Gaussians by Ashtiani, Ben-David, Harvey, Liaw, Mehrabian, and Plan arXiv:1710.05209.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jan 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3.
4 papers.
- 2023Private Distribution Learning with Public Data: The View from Sample Compressioncited by 14
- 2023Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samplescited by 5
- 2021Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistencycited by 3
- 2023Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivitycited by 3
- 2023Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity9 Dec 2023
- 2023Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples7 Sep 2023
- 2023Private Distribution Learning with Public Data: The View from Sample Compression11 Aug 2023
- 2021Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency8 Oct 2021