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Illustration de l'actualité : PhD defense Anna Korba : Learning from ranking data, theory and methods

PhD defense Anna Korba : Learning from ranking data, theory and methods

jeudi
25
octobre
2018

PhD Comics : I'm defending my thesis, Mom !

Jury

  • M. Stephan CLEMENÇON, Télécom ParisTech, PhD Director
  • M. Eyke HULLERMEIER, Paderborn University, Reviewer
  • M. Nicolas VAYATIS, ENS Cachan, Examiner
  • M. Jean-Philippe VERT, Google Brain, Examiner
  • Mme Shivani AGARWAL, University of Pennsylvania, Reviewer
  • Mme Florence D'ALCHÉ BUC, Télécom ParisTech, Examiner

Abstract

Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situations, especially when the data comes from human activities (ballots in political elections, survey answers, competition results) or in modern applications of data processing (search engines, recommendation systems). The design of machine-learning algorithms, tailored for these data, is thus crucial. However, due to the absence of any vectorial structure of the space of rankings, and its explosive cardinality when the number of items increases, most of the classical methods from statistics and multivariate analysis cannot be applied in a direct manner. Hence, a vast majority of the literature rely on parametric models. In this thesis, we propose a non-parametric theory and methods for ranking data.