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Towards Competitive N-gram Smoothing.

, , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 4206-4215. PMLR, (2020)

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Towards Competitive N-gram Smoothing., , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 4206-4215. PMLR, (2020)Optimal Sequential Maximization: One Interview is Enough!, , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 2975-2984. PMLR, (2020)Universal compression of power-law distributions., , , , and . ISIT, page 2001-2005. IEEE, (2015)Learning Markov distributions: Does estimation trump compression?, , , and . ISIT, page 2689-2693. IEEE, (2016)Near-Optimal Smoothing of Structured Conditional Probability Matrices., , and . NIPS, page 4853-4861. (2016)Maximum Selection and Ranking under Noisy Comparisons., , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 1088-1096. PMLR, (2017)The power of absolute discounting: all-dimensional distribution estimation., , , and . NIPS, page 6660-6669. (2017)Maxing and Ranking with Few Assumptions., , , , and . NIPS, page 7060-7070. (2017)The Limits of Maxing, Ranking, and Preference Learning., , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 1426-1435. PMLR, (2018)Faster Algorithms for Testing under Conditional Sampling., , , , and . CoRR, (2015)