上海科技大学知识管理系统

We propose the multi-level network Lasso, which aims to overcome the key limitations of existing personalized learning methods, such as ignoring sample homogeneity or heterogeneity, and over-parametrization. Multi-level network Lasso learns both sample-common model and sample-specific model, that are succinct and interpretable in the sense that model parameters are shared across neighboring samples based on only a subset of relevant features. To apply personalized learning in multi-task scenarios, we further extend the multi-level network Lasso for multi-task personalized learning by learning underlying task groups in the feature subspace. Additionally, we investigate a family of the multi-level network Lasso based on the & ell;(p) quasi-norm (0