English
High dimensional mean estimation - Lecture 2
Multi angle
Auteurs : Lugosi, Gábor (Auteur de la conférence)
CIRM (Editeur )
CIRM (Editeur )
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Résumé :
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.
Mots-Clés : network archeology; random trees; preferential attachment; broadcasting problem
Codes MSC : Mots-Clés : network archeology; random trees; preferential attachment; broadcasting problem
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de Publication : 14/01/2022 Date de Captation : 16/12/2021 Sous Collection : Research School Catégorie arXiv : Statistics ; Probability ; Computer Science Domaine(s) : Probabilités & Statistiques Format : MP4 (.mp4) - HD Durée : 00:53:47 Audience : Chercheurs Download : https://videos.cirm-math.fr/2021-12-15_Lugosi_Part2.mp4 
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Informations sur la Rencontre
Nom de la Rencontre : Meeting in Mathematical Statistics - Machine learning and nonparametric statistics / Rencontres de statistique mathématiqueOrganisateurs de la Rencontre : Butucea, Cristina ; Minsker, Stanislav ; Pouet, Christophe ; Spokoiny, Vladimir Dates : 13/12/2021 - 17/12/2021 Année de la rencontre : 2021 URL de la Rencontre : https://conferences.cirm-math.fr/2581.html
Données de citation
DOI : 10.24350/CIRM.V.19867803Citer cette vidéo: Lugosi, Gábor (2021). High dimensional mean estimation - Lecture 2. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19867803 URI : http://dx.doi.org/10.24350/CIRM.V.19867803 |
Voir Aussi
- [Multi angle] Adversarial examples in deep networks - Lecture 2 / Auteur de la conférence Bartlett, Peter.
- [Multi angle] Network archeology: a few results and questions - Lecture 1 / Auteur de la conférence Lugosi, Gábor.
- [Multi angle] Benign overfitting - Lecture 1 / Auteur de la conférence Bartlett, Peter.
Bibliographie
- 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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