Overview

Call for participation

Important Dates

Participants

Schedule

Accepted Papers

Resources

Sponsors

Inductive Transfer: 10 Years Later will be held as a post conference workshop following NIPS 2005, on Friday, December 9, at the Westin Resort and Spa in Whistler, British Columbia, Canada. 

Inductive transfer or transfer learning refers to the problem of retaining and applying the knowledge learned in one or more tasks
to efficiently develop an effective hypothesis for a new task. While all learning involves generalization across problem instances, transfer learning emphasizes the transfer of knowledge across domains, tasks, and distributions that are similar but not the same. For example, learning to recognize chairs might help to recognize tables; or learning to play checkers might improve the learning of chess. At NIPS95 a two-day workshop on "Learning to Learn" that focused on the need for lifelong machine learning methods that retain and reuse learned knowledge. This NIPS 2005 workshop will examine the progress that has been made in ten years, the questions and challenges that remain, and the opportunities for new applications of inductive transfer systems.

Please see the call for participation for more information on how to submit workshop papers.

Current highlights of the workshop are:
Invited speakers:
Rich Caruana -  Foundations / Survey of accepted theory
Shai Ben-David - New directions/Open questions, ML Theory Perspective
Theodoros Evgeniou - New directions/Open questions, Bayesian/Kernel Perspective
Tom Dietterich - Transfer Learning in the CALO Project (Cognitive Assistant that Learns and Organizes)
Panel/Discussion:
Morning: Progress? Why does learning with zero prior knowledge continue to dominate research?
Afternoon: What are the three most important problems to work on in future research?

A related workshop "Interclass Transfer: why learning to recognize many objects is easier than learning to recognize just one" will take place the following day, Saturday, December 10.  We are coordinating with the organizers of this other workshop so that they will provide complementary perspectives on these closely related topics.  This is great opportunity to learn more about an important and growing area of Machine Learning.