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Infinite-Task Learning with Vector-Valued RKHSs

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Abstract

Machine learning has witnessed the tremendous success of solving tasks depending on a hyperparameter. While multi-task learning is celebrated for its capacity to solve jointly a finite number of tasks, learning a continuum of tasks for various loss functions is still a challenge. A promising approach, called Parametric Task Learning, has paved the way in the case of piecewise-linear loss functions. We propose a generic approach, called Infinite-Task Learning, to solve jointly a continuum of tasks via vector-valued RKHSs. We provide generalization guarantees to the suggested scheme and illustrate its efficiency in cost-sensitive classification, quantile regression and density level set estimation.

Dates and versions

hal-01800203 , version 1 (25-05-2018)

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Romain Brault, Alex Lambert, Zoltan Szabo, Maxime Sangnier, Florence d'Alché-Buc. Infinite-Task Learning with Vector-Valued RKHSs. 2018. ⟨hal-01800203⟩
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