Mathematics Colloquium | High Dimensional multi-task learning, March 30

The Mathematics Colloquium series features Xin Gao of York University discussing "High Dimensional multi-task learning" on Thursday, March 30, from 4-5 p.m. via Zoom .

Abstract: Multi-task learning is the process of extracting information from multiple sources and analyzing different related data sets. Aggregating different data sources can boost the statistic power for the joint inference and enhance the model prediction performance.  In this talk, we will  focus on the development and implementation of both frequentist and Bayesian statistical methods for data integration and multi-task learning. We establish the estimation and model selection consistency of our proposed data integration methods. We also illustrate the use of the proposed methods on several high dimensional data sets.