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Appears in collection : Meeting in Mathematical Statistics - Machine learning and nonparametric statistics / Rencontres de statistique mathématique

Networks are often naturally modeled by random processes in which nodes and edges of the network are added one-by-one, according to some simple stochastic dynamics. Uniform and preferential attachment processes are prime examples of such dynamically growing networks. The statistical problems we address in this talk regard discovering the past of the network when a present-day snapshot is observed. Such problems are sometimes termed 'network archeology'. We present a few results that show that, even in gigantic networks, a lot of information is preserved from the very early days. As the field is still in its infancy, many interesting questions remain to be explored.

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Citation data

  • DOI 10.24350/CIRM.V.19867803
  • Cite this video Lugosi Gábor (12/16/21). High dimensional mean estimation - Lecture 2. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19867803
  • URL https://dx.doi.org/10.24350/CIRM.V.19867803


  • DEVROYE, Luc, LERASLE, Matthieu, LUGOSI, Gabor, et al. Sub-Gaussian mean estimators. The Annals of Statistics, 2016, vol. 44, no 6, p. 2695-2725. - https://doi.org/10.1214/16-AOS1440
  • LUGOSI, Gábor et MENDELSON, Shahar. Sub-Gaussian estimators of the mean of a random vector. The annals of statistics, 2019, vol. 47, no 2, p. 783-794. - https://doi.org/10.1214/17-AOS1639
  • LUGOSI, Gabor et MENDELSON, Shahar. Robust multivariate mean estimation: the optimality of trimmed mean. The Annals of Statistics, 2021, vol. 49, no 1, p. 393-410. - https://doi.org/10.1214/20-AOS1961
  • LUGOSI, Gabor et MENDELSON, Shahar. Multivariate mean estimation with direction-dependent accuracy. arXiv preprint arXiv:2010.11921, 2020. - https://arxiv.org/abs/2010.11921
  • LUGOSI, Gábor et MENDELSON, Shahar. Mean estimation and regression under heavy-tailed distributions: A survey. Foundations of Computational Mathematics, 2019, vol. 19, no 5, p. 1145-1190. - https://doi.org/10.1007/s10208-019-09427-x

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