No-Free-Lunch Theorems for Transfer Learning

author: Shai Ben-David, David R. Cheriton School of Computer Science, University of Waterloo
published: Jan. 19, 2010,   recorded: December 2009,   views: 6247


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I will present a formal framework for transfer learning and investigate under which conditions is it possible to provide performance guarantees for such scenarios. I will address two key issues:

  • 1) Which notions of task-similarity suffice to provide meaningful error bounds on a target task, for a predictor trained on a (different) source task?
  • 2) Can we do better than just train a hypothesis on the source task and analyze its performance on the target task? Can the use of unlabeled target samples reduce the target prediction error?

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