报告题目：Learning Feature Selection Dependencies in Multi-task Learning
报告人单位：Universidad Autónoma de Madrid, Spain
报告人简历：Daniel Hernández-Lobato received the degree of Engineer in Computer Science and the M.Sc. and Ph.D. degree in Computer Science from Universidad Autónoma de Madrid, Spain, in 2004, 2007 and 2009, respectively. Daniel was the recipient of an FPI grant from Consejería de Educación de la Comunidad de Madrid in 2005. Currently, he is assistant professor at Universidad Autónoma de Madrid, Spain. His research interests include pattern recognition, machine learning methods and Bayesian inference.
报告摘要：A probabilistic model based on the horseshoe prior is proposed for learning dependencies in the process of identifying relevant features for prediction. Exact inference is intractable in this model. However, expectation propagation offers an approximate alternative. Because the process of estimating feature selection dependencies may suffer from over-fitting in the model proposed, additional data from a multi-task learning scenario are considered for induction. The same model can be used in this setting with few modifications. Furthermore, the assumptions made are less restrictive than in other multi-task methods: The different tasks must share feature selection dependencies, but can have different relevant features and model coefficients. Experiments with real and synthetic data show that this model performs better than other multi-task alternatives from the literature. The experiments also show that the model is able to induce adequate feature selection dependencies for the problems considered, only from the training data.