Projects per year
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.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 665992
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 665992
Original language | English |
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Title of host publication | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
Publisher | IEEE |
Pages | 3465-3473 |
Number of pages | 9 |
Volume | 2018 |
ISBN (Electronic) | 9781538664209 |
ISBN (Print) | 9781538664216 |
DOIs | |
Publication status | Published - 17 Dec 2018 |
Event | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 - Duration: 18 Jun 2018 → 22 Jun 2018 |
Publication series
Name | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition) |
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Publisher | IEEE |
ISSN (Print) | 2575-7075 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 |
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Period | 18/06/18 → 22/06/18 |
Fingerprint
Dive into the research topics of 'Multi-task Learning by Maximizing Statistical Dependence'. Together they form a unique fingerprint.Projects
- 3 Finished
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Personalized Exploration of Imagery Database
Kim, K. I.
Engineering and Physical Sciences Research Council
1/09/16 → 31/05/17
Project: Research council
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Fellow for Industrial Research Enhancement (FIRE)
Scott, J. L. & Yang, Y.
1/10/15 → 30/03/21
Project: EU Commission
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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)
Cosker, D., Bilzon, J., Campbell, N., Cazzola, D., Colyer, S., Fincham Haines, T., Hall, P., Kim, K. I., Lutteroth, C., McGuigan, P., O'Neill, E., Richardt, C., Salo, A., Seminati, E., Tabor, A. & Yang, Y.
Engineering and Physical Sciences Research Council
1/09/15 → 28/02/21
Project: Research council