A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A stability index for feature selection
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Arabic handwritten digit recognition
International Journal on Document Analysis and Recognition
Feature Selection by Transfer Learning with Linear Regularized Models
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Tree ensembles for predicting structured outputs
Pattern Recognition
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation
The Journal of Machine Learning Research
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In this paper we propose a Bayesian model for multitask feature selection. This model is based on a generalized spike and slab sparse prior distribution that enforces the selection of a common subset of features across several tasks. Since exact Bayesian inference in this model is intractable, approximate inference is performed through expectation propagation (EP). EP approximates the posterior distribution of the model using a parametric probability distribution. This posterior approximation is particularly useful to identify relevant features for prediction. We focus on problems for which the number of features d is significantly larger than the number of instances for each task. We propose an efficient parametrization of the EP algorithm that offers a computational complexity linear in d. Experiments on several multitask datasets show that the proposed model outperforms baseline approaches for single-task learning or data pooling across all tasks, as well as two state-of-the-art multi-task learning approaches. Additional experiments confirm the stability of the proposed feature selection with respect to various sub-samplings of the training data.