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We study the problem of computing the maximum likelihood estimator (MLE) of multivariate log-concave densities.
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Theoretical properties of the log-concave maximum likelihood estimator of a multidimensional density
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Introductory lectures on stochastic convex optimization
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Global rates of convergence of the mles of log-concave and s s -concave densities
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Approximation and estimation of s s -concave densities via renyi divergences
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Adaptation in log-concave density estimation
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Global rates of convergence in log-concave density estimation
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Sample-optimal density estimation in nearly-linear time
J. Acharya, I. Diakonikolas, J. Li, and L. Schmidt · 2017
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Learning multivariate log-concave distributions
I. Diakonikolas, D. M. Kane, and A. Stewart · 2017
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Geometry of Log-Concave Density Estimation
E. Robeva, B. Sturmfels, and C. Uhler · 2017
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Recent progress in log-concave density estimation
R. J. Samworth · 2017
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An efficient algorithm for high-dimensional log-concave maximum likelihood
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Fast and sample near-optimal algorithms for learning multidimensional histograms
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