Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Predicting patients' risk of develo** certain diseases is an important research topic in
healthcare. Accurately identifying and ranking the similarity among patients based on their …

We introduce the localized Lasso, which learns models that both are interpretable and have
a high predictive power in problems with high dimensionality d and small sample size n …

Alzheimer's disease (AD) has been not only a substantial financial burden to the health care
system but also the emotional hardship to patients and their families. Predicting cognitive …

Feature selection is effective in preparing high-dimensional data for a variety of learning
tasks such as classification, clustering and anomaly detection. A vast majority of existing …

Multi-task learning learns multiple related tasks together, in order to improve the
generalization performance. Existing methods typically build a global model shared by all …

Modern applications of machine learning (ML) deal with increasingly heterogeneous
datasets comprised of data collected from overlap** latent subpopulations. As a result …