Multitask Learning

Add to your list(s) Download to your calendar using vCal

• Prof Massimiliano Pontil - University College London
• Wednesday 07 May 2014, 14:00-15:00
• Lecture Theatre 1, Computer Laboratory.

If you have a question about this talk, please contact David Greaves.

Machine learning studies the problem of learning to perform a given task from a dataset of examples. A fundamental limitation of standard machine learning methods is the cost incurred in preparing large training datasets. Often in applications a limited number of examples is available and the task cannot be solved in isolation. A potential remedy is offered by multitask learning, which aims to learn several related tasks simultaneously. If the tasks share some constraining or generative property which is sufficiently simple it should allow for better learning of the individual tasks even when the individual training datasets are small. In the talk, I will present a wide class of multitask learning methods which encourage different forms of task relatedness and involve certain notions of structured sparsity and low rank tensor representations. I will also discuss iterative algorithms to implement these methods, building upon ideas from convex optimisation. Finally, I will illustrate the performance of these methods in applications arising in affective computing, computer vision and user modelling.

This talk is part of the Wednesday Seminars - Department of Computer Science and Technology series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge talks
• Chris Davis' list
• Department of Computer Science and Technology talks and seminars
• Graduate-Seminars
• Guy Emerson's list
• Interested Talks
• Lecture Theatre 1, Computer Laboratory
• School of Technology
• Trust & Technology Initiative - interesting events
• Wednesday Seminars - Department of Computer Science and Technology
• bld31
• computer science
• se393's list

Note that ex-directory lists are not shown.