Name
Abstract | ||
|---|---|---|
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 multitask 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. |
Year | Venue | DocType |
|---|---|---|
2015 | International Conference on Machine Learning | Conference |
Citations | PageRank | References |
7 | 0.41 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Hernández-Lobato | 1 | 440 | 26.10 |
| José Miguel Hernández-Lobato | 2 | 613 | 49.06 |
| Zoubin Ghahramani | 3 | 10455 | 1264.39 |