Multi-task Learning by Maximizing Statistical Dependence

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

We present a new multi-task learning (MTL) approach that can be applied to multiple heterogeneous task estimators. Our motivation is that the best task estimator could change depending on the task itself. For example, we may have a deep neural network for the first task and a Gaussian process for the second task. Classical MTL approaches cannot handle this case, as they require the same model or even the same parameter types for all tasks. We tackle this by considering task-specific estimators as random variables. Then, the task relationships are discovered by measuring the statistical dependence between each pair of random variables. By doing so, our model is independent of the parametric nature of each task, and is even agnostic to the existence of such parametric formulation. We compare our algorithm with existing MTL approaches on challenging real world ranking and regression datasets, and show that our approach achieves comparable or better performance without knowing the parametric form.
Original languageEnglish
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherIEEE
Pages3465-3473
Number of pages9
Volume2018
ISBN (Electronic)9781538664209
ISBN (Print)9781538664216
DOIs
Publication statusPublished - 17 Dec 2018
EventIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 -
Duration: 18 Jun 201822 Jun 2018

Publication series

NameProceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition)
PublisherIEEE
ISSN (Print)2575-7075

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018
Period18/06/1822/06/18

Projects

  • Cite this

    Alami Mejjati, Y., Cosker, D., & Kim, K. I. (2018). Multi-task Learning by Maximizing Statistical Dependence. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (Vol. 2018, pp. 3465-3473). (Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition)). IEEE. https://doi.org/10.1109/CVPR.2018.00365