Focused Multi-task Learning Using Gaussian Processes

Talk announcement
HIIT Seminar Kumpula, Friday October 21 10:15, Exactum B222
(Please notice the new date!)

SPEAKER:
Jaakko Peltonen
Aalto University

TITLE:
Focused Multi-task Learning Using Gaussian Processes

*** This work by Gayle Leen, Jaakko Peltonen, and Samuel Kaski 
won the Award for Best Paper in Machine Learning at ECML PKDD 2011, 
the European Conference on Machine Learning and Principles and 
Practice of Knowledge Discovery in Databases. ***

ABSTRACT:
Given a learning task for a data set, learning it together with 
related tasks (data sets) can improve performance. Gaussian 
process models have been applied to such multi-task learning 
scenarios, based on joint priors for functions underlying the 
tasks. In previous Gaussian process approaches, all tasks have 
been assumed to be of equal importance, whereas in transfer 
learning the goal is asymmetric: to enhance performance on a 
target task given all other tasks. In both settings, transfer 
learning and joint modeling, negative transfer is a key problem: 
performance may actually decrease if the tasks are not related 
closely enough. In this paper, we propose a Gaussian process model 
for the asymmetric setting, which learns to “explain away” 
non-related variation in the additional tasks, in order to focus on 
improving performance on the target task. In experiments, our model 
improves performance compared to single-task learning, symmetric 
multi-task learning using hierarchical Dirichlet processes, and 
transfer learning based on predictive structure learning.