Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)
Xiaolin Yang, Seyoung Kim, Eric Xing
Multitask learning addressed the problem of learning related tasks whose information can be shared each other. Traditional problem usually deal with homogeneous tasks such as regression, classification individually. In this paper we consider the problem learning multiple related tasks where tasks consist of both continuous and discrete outputs from a common set of input variables that lie in a high-dimensional space. All of the tasks are related in the sense that they share the same set of relevant input variables, but the amount of influence of each input on different outputs may vary. We formulate this problem as a combination of linear regression and logistic regression and model the joint sparsity as L1/Linf and L1/L2-norm of the model parameters. Among several possible applications, our approach addresses an important open problem in genetic association mapping, where we are interested in discovering genetic markers that influence multiple correlated traits jointly. In our experiments, we demonstrate our method in the scenario of association mapping, using simulated and asthma data, and show that the algorithm can effectively recover the relevant inputs with respect to all of the tasks.