2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

Collection 2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

Organisateur(s) Aamari, Eddie ; Aaron, Catherine ; Chazal, Frédéric ; Fischer, Aurélie ; Hoffmann, Marc ; Le Brigant, Alice ; Levrard, Clément ; Michel, Bertrand

Date(s) 03/10/2022 - 07/10/2022

URL associée https://indico.math.cnrs.fr/event/7545/

13 21

Optimal Permutation estimation in crowdsourcing problems

De Nicolas Verzelen

Motivated by crowd-sourcing applications, we consider a model where we have partial observations from a bivariate isotonic $n\times d$ matrix with an unknown permutation $\pi^_$ acting on its rows. We consider the twin problems of recovering the permutation $\pi^_$ and estimating the unknown matrix. We introduce a polynomial-time procedure achieving the minimax risk for these two problems, this for all possible values of $n$, $d$, and all possible sampling efforts. Along the way, we establish that, in some regimes, recovering the unknown permutation $\pi^*$ is considerably simpler than estimating the matrix.

This is based on a joint work with Alexandra Carpentier (U. Potsdam) and Emmanuel Pilliat (U. Montpellier).

Informations sur la vidéo

Date de captation 06/10/2022
Date de publication 03/05/2024
Institut IHP
Langue Anglais
Audience Chercheurs
Réalisateur(s) Alexandre Duplessis, Marco Perez
Format MP4
Lieu Amphitheater Hermite, IHP

Données de citation

DOI 10.57987/IHP.2022.T3.WS1.013
Citer cette vidéo Verzelen, Nicolas (06/10/2022). Optimal Permutation estimation in crowdsourcing problems. IHP. Audiovisual resource. DOI: 10.57987/IHP.2022.T3.WS1.013
URL https://dx.doi.org/10.57987/IHP.2022.T3.WS1.013

Domaine(s)

Apprentissage

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