CUED Publications database

A probabilistic model for dirty multi-task feature selection

Hernández-Lobato, D and Hernández-Lobato, JM and Ghahramani, Z (2015) A probabilistic model for dirty multi-task feature selection. In: UNSPECIFIED pp. 1073-1082..

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Copyright © 2015 by the author(s). Multi-task feature selection methods often make the hypothesis that learning tasks share relevant and irrelevant features. However, this hypothesis may be too restrictive in practice. For example, there may be a few tasks with specific relevant and irrelevant features (outlier tasks). Similarly, a few of the features may be relevant for only some of the tasks (outlier features). To account for this, we propose a model for multi-task feature selection based on a robust prior distribution that introduces a set of binary latent variables to identify outlier tasks and outlier features. Expectation propagation can be used for efficient approximate inference under the proposed prior. Several experiments show that a model based on the new robust prior provides better predictive performance than other benchmark methods.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 18:59
Last Modified: 18 Aug 2020 13:56