Timezone: »
Poster
Joint quantile regression in vector-valued RKHSs
Maxime Sangnier · Olivier Fercoq · Florence d'Alché-Buc
Addressing the will to give a more complete picture than an average relationship provided by standard regression, a novel framework for estimating and predicting simultaneously several conditional quantiles is introduced. The proposed methodology leverages kernel-based multi-task learning to curb the embarrassing phenomenon of quantile crossing, with a one-step estimation procedure and no post-processing. Moreover, this framework comes along with theoretical guarantees and an efficient coordinate descent learning algorithm. Numerical experiments on benchmark and real datasets highlight the enhancements of our approach regarding the prediction error, the crossing occurrences and the training time.
Author Information
Maxime Sangnier (LTCI)
Olivier Fercoq (Telecom ParisTech)
Florence d'Alché-Buc (Télécom Paris, Institut Polytechnique de Paris, France)
More from the Same Authors
-
2019 Poster: Stochastic Frank-Wolfe for Composite Convex Minimization »
Francesco Locatello · Alp Yurtsever · Olivier Fercoq · Volkan Cevher -
2018 Poster: A Structured Prediction Approach for Label Ranking »
Anna Korba · Alexandre Garcia · Florence d'Alché-Buc -
2017 Workshop: Learning on Distributions, Functions, Graphs and Groups »
Florence d'Alché-Buc · Krikamol Muandet · Bharath Sriperumbudur · Zoltán Szabó -
2017 Poster: Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization »
Ahmet Alacaoglu · Quoc Tran Dinh · Olivier Fercoq · Volkan Cevher -
2016 Poster: GAP Safe Screening Rules for Sparse-Group Lasso »
Eugene Ndiaye · Olivier Fercoq · Alexandre Gramfort · Joseph Salmon -
2015 Poster: GAP Safe screening rules for sparse multi-task and multi-class models »
Eugene Ndiaye · Olivier Fercoq · Alexandre Gramfort · Joseph Salmon