Speaker: Rich Caruana

Affiliation: CS Dept., Carnegie Mellon University

Date: 9/21/00

Time and Location: 4:15pm, B17 Upson Hall

Title: Multitask Learning

Abstract:

If it is hard to learn one problem, certainly it is harder to learn 100 problems. This is not necessarily true; what you learn for one problem can make learning another problem easier. Multitask Learning is a machine learning method that learns each problem better by also learning from the training signals of *other* related problems. It does this by learning all of the problems in parallel while using a shared representation; what is learned for each problem helps other problems be learned better.

In the talk I demonstrate multitask learning on a half-dozen problems. Two of these are real problems in medical decision making for which multitask learning currently outperforms all other methods. I explain how multitask learning works, and show that there are many ways to use it on real problems, some of which are rather surprising. I'll also present suggestions for how to get the most out of multitask learning in artificial neural nets, present an algorithm for multitask learning with case-based methods like k-nearest neighbor and kernel regression, and sketch an algorithm for multitask learning in decision trees.