1. Transfer Learning Motivation and Types Functional Transfer Learning Representational Transfer Learning References

2. The goal is to transfer knowledge gathered from previous experience. Also called Inductive Transfer or Learning to Learn. Example: Invariant transformations across tasks. Transfer Learning

3. Motivation for transfer learning Similar to self-adaptation: once a predictive model is built, there are reasons to believe the model will cease to be valid at some point in time. The difference is that now source and target domains can be completely different. Motivation Transfer Learning

4. Traditional Approach to Classification DB1DB2DBn Learning System

5. Transfer Learning DB1DB2 DB new Learning System Knowledge Source domain Target domain

6. Transfer Learning Scenarios: 1.Labeling in a new domain is costly. DB1 (labeled) Classification of Cepheids DB2 (unlabeled) Classification of LPV

7. Transfer Learning Scenarios: 2. Data is outdated. Model created with one survey but a new survey is now available. Survey 1 Learning System Survey 2 ?

8. Types of Transfer Learning Figure obtained from Brazdil, et. Al. Metalearning: Applications to Data Mining, Chapter 7, Springer, 2009.

9. Transfer Learning Motivation and Types Functional Transfer Learning Representational Transfer Learning References

10. Input nodes Internal nodes Output nodes LeftStraightRight Functional Transfer: Multitask Learning

11. Given example X, compute the output of every node until we reach the output nodes: Input nodes Internal nodes Output nodes Example X Compute sigmoid function function Functional Transfer in Neural Networks

12. Train in Parallel with Combined Architecture Figure obtained from Brazdil, et. Al. Metalearning: Applications to Data Mining, Chapter 7, Springer, 2009.

13. Transfer Learning Motivation and Types Functional Transfer Learning Representational Transfer Learning References

14. Knowledge of Parameters Assume prior distribution of parameters Source domain Learn parameters and adjust prior distribution Target domain Learn parameters using the source prior distribution.

15. P(y|x) = P(x|y) P(y) / P(x) Parameter Similarity Task A  Parameter A Task B  Parameter B ~ A Assume hyper-distribution with low variance. Assume Parameter Similarity

16. Knowledge of Parameters Find coefficients w s using SVMs Find coefficients w T using SVMs initializing the search with w s

17. Feature Transfer Feature Transfer: Target domain Source domain Shared representation across tasks Minimize Loss-Function( y, f(x)) The minimization is done over multiple tasks (multiple regions on Mars).

18. Feature Transfer Identify common Features to all tasks

19. Coded divided into pieces New Solution Add pieces of code from previous tasks Start a new solution from scratch Meta-Searching for Problem Solvers

20. Exploitation: Maximize reward vs Exploration: Maximize long-term success.

21. Learn to keep the ball away from the opponent. First Task Learn to score the opponent. Second Task Transfer Learning in Robotics

22. Instance Transfer Learning Instance Transfer: Learning System Target domain Source domain Filter samples Larger target dataset New program called TrAdaboost

23. Instance Transfer Learning New program called TrAdaboost The main idea is to have a methodology to deal with a changing distribution. Examples in the source domain that look as belonging to a diff. distribution are discarded. Examples in the source domain that look similar to the target domain are added to the training set.

24. Boosting DB Incorrectly classified instances  increase weight 1 1 1 11 1 1 1 11 11 1 1 1 2 2 2 22

25. Boosting DB 1 1 1 1 1 2 2 2 2 2 2 1 1 Combine all hypotheses to produce final weighted function: w 1 f 1 + w 2 f 2 + … + w n f n

26. Automatic Instance Transfer Boosting Source domain Target domain Learning System (Boosting) Incorrectly classified instances  decrease weight Incorrectly classified instances  increase weight Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007

27. Automatic Instance Transfer Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007

28. Automatic Instance Transfer Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007

29. Automatic Instance Transfer Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007

30. Automatic Instance Transfer Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007

31. Automatic Instance Transfer Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007

32. Automatic Instance Transfer Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007

33. Transfer Learning Motivation and Types Functional Transfer Learning Representational Transfer Learning References

34. Sinno Jialin Pan and Qiang Yang. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345-1359, Oct. 2010 Brazdil, P. et. al. Metalearning: Applications to Data Mining. Springer, 2009. Dai, W., Boosting for Transfer Learning, Proceedings of ICML 2007. Video on transfer learning http://www.youtube.com/watch?v=9ChVn3xVNDI&noredirect=1 References

35. Sinno Jialin Pan and Qiang Yang. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345-1359, Oct. 2010 Brazdil, P. et. al. Metalearning: Applications to Data Mining. Springer, 2009. Dai, W., Boosting for Transfer Learning, Proceedings of ICML 2007. Video on transfer learning http://www.youtube.com/watch?v=9ChVn3xVNDI&noredirect=1 References

36. Robot learns to flip pancakes http://www.youtube.com/watch?v=W_gxLKSsSIE&noredirect=1 Robot learns to stack pancakes http://www.youtube.com/watch?v=v9oeOYMRvuQ Videos