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