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We study the {\em robust proper learning} of univariate log-concave distributions (over continuous and discrete domains).
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The geometry of logconcave functions and sampling algorithms
L. Lovász and S. Vempala · 2007
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L. Dumbgen and K. Rufibach · 2009
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On the rate of convergence of the maximum likelihood estimator of a k k -monotone density
F. Gao and J. A. Wellner · 2009
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G. Walther · 2009
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Efficiently learning mixtures of two Gaussians
A. T. Kalai, A. Moitra, and G. Valiant · 2010
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logcondens: Computations related to univariate log-concave density estimation
L. Dümbgen and K. Rufibach · 2011
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Faster and sample near-optimal algorithms for proper learning mixtures of Gaussians
C. Daskalakis and G. Kamath · 2014
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Global rates of convergence in log-concave density estimation
A. K. H. Kim and R. J. Samworth · 2014
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Near-optimal-sample estimators for spherical gaussian mixtures
A. T. Suresh, A. Orlitsky, J. Acharya, and A. Jafarpour · 2014
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Log-concavity and strong log-concavity: A review
A. Saumard and J. A. Wellner · 2014
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Optimal testing for properties of distributions
J. Acharya, C. Daskalakis, and G. Kamath · 2015
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Hardness of proper learning (1988; pitt, valiant)
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