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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Topics and Techniques in Distribution Testing: A Biased but Representative Sample.\n \n \n \n \n\n\n \n Canonne, C. L.\n\n\n \n\n\n\n Foundations and Trends® in Communications and Information Theory, 19(6): 1032-1198. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"TopicsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 155 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{CanonneTopicsDT2022,\n\turl = {http://dx.doi.org/10.1561/0100000114},\n\tyear = {2022},\n\tvolume = {19},\n\tjournal = {Foundations and Trends® in Communications and Information Theory},\n\ttitle = {Topics and Techniques in Distribution Testing: A Biased but Representative Sample},\n\tdoi = {10.1561/0100000114},\n\tissn = {1567-2190},\n\tnumber = {6},\n\tpages = {1032-1198},\n\tauthor = {Clément L. Canonne}\n}\n
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\n  \n 2020\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n A Survey on Distribution Testing: Your Data is Big. But is it Blue?.\n \n \n \n \n\n\n \n Canonne, C. L.\n\n\n \n\n\n\n Theory of Computing, (9): 1–100. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 396 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{Canonne:Survey:ToC,\n author = {Cl{\\'{e}}ment L. Canonne},\n title = {A Survey on Distribution Testing: Your Data is Big. But is it Blue?},\n year = {2020},\n pages = {1--100},\n doi = {10.4086/toc.gs.2020.009},\n publisher = {Theory of Computing Library},\n journal = {Theory of Computing},\n number = {9},\n series = {Graduate Surveys},\n url = {http://www.theoryofcomputing.org/articles/gs009/},\n}\n\n
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