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How much data is sufficient to learn high-performing algorithms?, , , , , и . CoRR, (2019)Efficient Representations for Lifelong Learning and Autoencoding., , и . COLT, том 40 из JMLR Workshop and Conference Proceedings, стр. 191-210. JMLR.org, (2015)k-Center Clustering Under Perturbation Resilience., , и . ICALP, том 55 из LIPIcs, стр. 68:1-68:14. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2016)Performance guarantees for transferring representations., и . ICLR (Workshop), OpenReview.net, (2017)Nash Equilibria in Perturbation-Stable Games., и . Theory of Computing, 13 (1): 1-31 (2017)Communication Efficient Distributed Agnostic Boosting., , и . AISTATS, том 51 из JMLR Workshop and Conference Proceedings, стр. 1299-1307. JMLR.org, (2016)Distributed Learning, Communication Complexity and Privacy., , , и . COLT, том 23 из JMLR Proceedings, стр. 26.1-26.22. JMLR.org, (2012)A Theory of Loss-Leaders: Making Money by Pricing Below Cost., , , и . WINE, том 4858 из Lecture Notes in Computer Science, стр. 293-299. Springer, (2007)Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks., , , , , и . J. Mach. Learn. Res., (2017)Geometry-Aware Gradient Algorithms for Neural Architecture Search., , , и . CoRR, (2020)