Transfer Learning for Complex Tasks

An AAAI-08 workshop.

All machine learning algorithms require data to learn and often the amount of data available is a limiting factor. Classification requires labeled data, which may be expensive to obtain. Reinforcement learning requires samples from an environment which takes time to gather. Recently, transfer learning (TL) approaches have been gaining in popularity as an approach to increase learning performance. Rather than learning a novel target task in isolation, transfer approaches make use of data from one or more source tasks in order to learn the target task with less data or to achieve a higher performance level.

While transfer has long been studied in humans, it was first applied as a machine learning technique only in the mid-nineties. Although TL is making rapid progress, there are a number of open questions in the field, including:

  1. How can an appropriate source task be selected for a given target task?
  2. In some situations transfer decreases performance. Is it possible to avoid negative transfer?
  3. How can one learn the relationship between a given source and target task, if such a relationship exists?
  4. What characteristics determine the effectiveness of transfer?

This workshop will give researchers working in TL an opportunity to both present their work and to discuss current topics of interest. We solicit papers that demonstrate empirical success in transferring knowledge between complex tasks, or introduce transfer methods that are likely to scale to such problems. We are most interested in work which examines transfer between reinforcement learning agents, but transfer between any machine learning algorithms will be in scope for this workshop. All submissions will be reviewed for relevance, originality, significance, and clarity. Work will be accepted for either oral or poster presentation.

Each paper will be allocated 20 minutes for a talk and 10 minutes for questions.

Important dates

Organizing Committee: Matthew E. Taylor (primary contact, The University of Texas at Austin), Alan Fern (Oregon State University), and Kurt Driessens (K. U. Leuven)

Senior Steering Committee: Peter Stone (The University of Texas at Austin), Richard Maclin (The University of Minnesota), and Jude Shavlik (The University of Wisconsin at Madison)