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Simply put, we found that WordPress dominates the list, that Movable Type comes in with a respectable second, and the rest are either custom jobbies or a smattering of other platforms which are, relatively speaking, eating dust.
C. Canonne. (2020)cite arxiv:2002.11457Comment: This is a review article; its intent is not to provide new results, but instead to gather known (and useful) ones, along with their proofs, in a single convenient location.
M. Brennan, and G. Bresler. (2020)cite arxiv:2005.08099Comment: 175 pages; subsumes preliminary draft arXiv:1908.06130; accepted for presentation at the Conference on Learning Theory (COLT) 2020.
S. Mukhopadhyay, and K. Wang. (2020)cite arxiv:2004.09588Comment: We'd love to hear your feedback. Email us. (We thank those who have already sent us their comments.).
A. Decadt, G. de Cooman, and J. De Bock. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, volume 103 of Proceedings of Machine Learning Research, page 135--144. Thagaste, Ghent, Belgium, PMLR, (03--06 Jul 2019)
B. Axelrod, I. Diakonikolas, A. Sidiropoulos, A. Stewart, and G. Valiant. (2019)cite arxiv:1907.08306Comment: The present paper is a merger of two independent works arXiv:1811.03204 and arXiv:1812.05524, proposing essentially the same algorithm to compute the log-concave MLE.