Mathematical methods of modern statistics / Méthodes mathématiques en statistiques modernes

Collection Mathematical methods of modern statistics / Méthodes mathématiques en statistiques modernes

Organizer(s) Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne ; Wesolowski, Jacek

Date(s) 10/07/2017 - 14/07/2017

linked URL http://conferences.cirm-math.fr/1487.html

00:00:00 / 00:00:00

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Learning on the symmetric group

By Jean-Philippe Vert

Many data can be represented as rankings or permutations, raising the question of developing machine learning models on the symmetric group. When the number of items in the permutations gets large, manipulating permutations can quickly become computationally intractable. I will discuss two computationally efficient embeddings of the symmetric groups in Euclidean spaces leading to fast machine learning algorithms, and illustrate their relevance on biological applications and image classification.

Information about the video

Date of recording 13/07/2017
Date of publication 20/07/2017
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19194703
Cite this video VERT, Jean-Philippe (13/07/2017). Learning on the symmetric group. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19194703
URL https://dx.doi.org/10.24350/CIRM.V.19194703

Domain(s)

Bibliography

Barlow, R.E., Bartholomew, D.J., Bremner, J.M., & Brunk, H.D. (1972). Statistical inference under order restrictions. The theory and application of isotonic regression. London: John Wiley & Sons - https://zbmath.org/?q=an:0246.62038
Sysoev, O., & Burdakov, O. (2016). A smoothed monotonic regression via l2 regularization. Technical Report LiTH-MAT-R–2016/01–SE, Department of mathematics, Linköping University - http://liu.diva-portal.org/smash/get/diva2:905380/FULLTEXT01.pdf
Tan, A.C., Naiman, D.Q., Xu, L., Winslow, R.L., & and Geman, D. (2005). Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics, 21(20), 3896-3904 - http://dx.doi.org/10.1093/bioinformatics/bti631

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