Magnet seminars are usually held in room B21 on Thursdays, 11am. Check below for upcoming seminars and potential changes of schedule/location. You may also import the Magnet seminars public feed into your favorite calendar app. For further information, contact Aurélien.
Thu, November 7, 2019
Where? Inria B21
Label ranking is the supervised problem of learning a mapping from a general feature space to the space of full rankings. In this talk, I will briefly explain the key concepts of ranking data, and present two families of (non-parametric) methods we developed for label ranking. The first one  adapts well-known partition methods (k-nearest neighbor and tree-based methods) for ranking data. These predictive rules build a partition of the feature space from the data, and compute efficiently (approximate) Kemeny ranking aggregation at a local level. The second one  adopts the least square surrogate loss approach from structured prediction, that solves a supervised learning problem in two steps: a regression step in a well-chosen feature space and a pre-image (or decoding) step. We use specific feature maps/embeddings for ranking data, which convert any ranking/permutation into a vector representation.
 Ranking Median Regression: Learning to Order through Local Consensus. (ALT 2018) Stephan Clémençon, Anna Korba and Eric Sibony.
 A Structured Prediction Approach for Label Ranking. (NIPS 2018) Anna Korba, Alexandre Garcia, Florence D’Alché Buc.
Thursday, November 7, 2019 - 11:00 to 12:00