Efficient density estimation via piecewise polynomial approximation

DOI10.1145/2591796.2591848zbMath1315.68163arXiv1305.3207OpenAlexW2143122862MaRDI QIDQ5259596

Xiaorui Sun, Ilias Diakonikolas, Siu-On Chan, Rocco A. Servedio

Publication date: 26 June 2015

Published in: Proceedings of the forty-sixth annual ACM symposium on Theory of computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1305.3207

zbMATH Keywords

computational learning theoryunsupervised learninglearning distributions

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (9)

Sampling Correctors ⋮ Monte Carlo Gradient in Optimization Constrained by Radiative Transport Equation ⋮ On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising ⋮ Testing shape restrictions of discrete distributions ⋮ Robust Estimators in High-Dimensions Without the Computational Intractability ⋮ Maximum a posteriori estimators as a limit of Bayes estimators ⋮ Unnamed Item ⋮ Piecewise linear approximation of empirical distributions under a Wasserstein distance constraint ⋮ Reliable clustering of Bernoulli mixture models

Uses Software

• SuLQ

Cites Work

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