M. Brennan, и 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, и 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.).
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.
I. Diakonikolas, D. Kane, и A. Stewart. (2016)cite arxiv:1611.03473Comment: Changes from v1: Revised presentation. Added more applications of the technique (SQ lower bounds for robust sparse mean estimation and robust covariance estimation in spectral norm). Sharpened testing lower bound to linear in the dimension (compared to nearly-linear in first version).
B. Axelrod, I. Diakonikolas, A. Sidiropoulos, A. Stewart, и 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.
S. Kamath, A. Orlitsky, D. Pichapati, и A. Suresh. Proceedings of The 28th Conference on Learning Theory, том 40 из Proceedings of Machine Learning Research, стр. 1066--1100. Paris, France, PMLR, (03--06 Jul 2015)
A. Fisher, и E. Kennedy. (2018)cite arxiv:1810.03260Comment: This manuscript version includes 2 additional supplemental figures to further aid intuition. In total: 4 figures, 36 pages (double spaced).
A. Decadt, G. de Cooman, и J. De Bock. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, том 103 из Proceedings of Machine Learning Research, стр. 135--144. Thagaste, Ghent, Belgium, PMLR, (03--06 Jul 2019)