A Comparison of Methods for Transductive Transfer Learning

1. A Comparison of Methods for Transductive Transfer Learning Andrew Arnold Advised by William W. Cohen Machine Learning Department School of Computer Science Carnegie Mellon University May 30, 2007

2. What we are able to do: • Supervised learning • Train on large, labeled data sets drawn from same distribution as testing data • Well studied problem Training data: Test: Test: Train: Reversible histone acetylation changes the chromatin structure and can modulate gene transcription. Mammalian histone deacetylase 1 (HDAC1) The neuronal cyclin-dependent kinase p35/cdk5 comprises a catalytic subunit (cdk5) and an activator subunit (p35)

3. What we’re getting better at doing: • Semi-supervised learning • Same as before, but now • Add large unlabelled or weakly labeled data sets from same domain • [Zhu ’05, Grandvalet ’05] Train: Auxiliary (available for training): Auxiliary: Train: Reversible histone acetylation changes the chromatin structure and can modulate gene transcription. Mammalian histone deacetylase 1 (HDAC1) The neuronal cyclin-dependent kinase p35/cdk5 comprises a catalytic subunit (cdk5) and an activator subunit (p35)

4. What we’re getting better at doing: • Transductive learning • Unlabeled test data is available during training • Easier than inductive learning: • Learning specific predictions rahter than general function • [Joachims ’99, ’03, Sindhwani ’05, Vapnik ‘98] Train: Both Auxiliary & Eventual Test: Auxiliary & Test: Train: Reversible histone acetylation changes the chromatin structure and can modulate gene transcription. Mammalian histone deacetylase 1 (HDAC1) The neuronal cyclin-dependent kinase p35/cdk5 comprises a catalytic subunit (cdk5) and an activator subunit (p35)

5. What we’d like to be able to do: • Transfer learning (domain adaptation): • Leverage large, previously labeled data from a related domain • Related domain we’ll be training on (with lots of data): Source • Domain we’re interested in and will be tested on (data scarce): Target • [Ng ’06, Daumé ’06, Jiang ’06, Blitzer ’06, Ben-David ’07, Thrun ’96] Train (source domain: E-mail): Test (target domain: IM): Test (target domain: Caption): Train (source domain:Abstract): Neuronal cyclin-dependent kinase p35/cdk5 (Fig 1, a) comprises a catalytic subunit (cdk5, left panel) and an activator subunit (p35, fmi #4) The neuronal cyclin-dependent kinase p35/cdk5 comprises a catalytic subunit (cdk5) and an activator subunit (p35)

6. What we’d like to be able to do: • Transfer learning (multi-task): • Same domain, but slightly different task • Related task we’ll be training on (with lots of data): Source • Task we’re interested in and will be tested on (data scarce): Target • [Ando ’05, Sutton ’05] Train (source task: Names): Test (target task: Pronouns): Test (target task: Action Verbs): Train (source task:Proteins): Reversible histone acetylation changes the chromatin structure and can modulate gene transcription. Mammalian histone deacetylase 1 (HDAC1) The neuronal cyclin-dependent kinase p35/cdk5 comprises a catalytic subunit (cdk5) and an activator subunit (p35)

7. Motivation • Why is transfer important? • Often we violate non-transfer assumption without realizing. How much data is truly identically distributed (i.i.d.)? • E.g. Different authors, annotators, time periods, sources • Large amounts of labeled data/trained classifiers already exist • Why waste data & computation? • Can learning be made easier by leveraging related domains/problems? • Life-long learning • Why is transduction important? • Why solve a harder problem than we need to? • Unlabeled data is vast and cheap • Are transduction and transfer so different? • Can we learn more about one by studying the other?

8. Outline • Motivating Problems • Supervised learning • Semi-supervised learning • Transductive learning • Transfer learning: domain adaptation • Transfer learning: multi-task • Methods • Maximum entropy (MaxEnt) • Source regularized maximum entropy • Feature space expansion • Feature selection • Feature space transformation • Iterative Pseudo Labeling (IPL) • Biased thresholding • Support Vector Machines (SVMs) • Inductive SVM • Transductive SVM • Experiment: • Domain & Data • Results • Conclusions & Contributions • Limitations & future work

9. Maximum Entropy (MaxEnt) • Discriminative model • Matches feature expectations of model to data Conditional likelihood: Regularized optimization:

10. Summary of Learning Settings 10

11. Source-regularized MaxEnt • Instead of regularizing towards zero • Learn model Λ’s on source data • During target training • Regularize towards source-trained Λ’s • [Chelba’04]

12. Feature Space Expansion • Add extra degrees of freedom • Allow classifier to discern general/specific features [Daumé ’06, ’07]

13. Feature selection • Emphasize features shared by source and target data • Minimize different features • How to measure? • Fisher exact test: Is P(feature | source) == P(feature | target) ? • If so, shared feature  keep • If not, different feature  discard

14. Feature Space Transformation • Source and target originally independently separable • Learn transformation, G, to allow joint separation:

15. Iterative Pseudo Labeling (IPL) • Novel algorithm for MaxEnt based transfer • Adjust feature values to match feature expectation in source and target • θ trades off certainty vs adaptativity

16. IPL analysis Given linear transform: We can express conditional feature expectations of target data in terms of a transformation of source:

17. Biased Thresholding • Different proportions of positive examples • Learning to predict rain in in humid and arid climates • How to maximize F1 (and not accuracy)? • Score Cut (s-cut) • Select score threshold over ranked train scores • Apply to test data • Percentage Cut (p-cut) • Estimate proportion of positive examples expected in target data • Set threshold so as to select this amount

18. Support Vector Machines (SVMs) • Inductive (standard) SVM: • Learn separating hyperplane on labeled training data. Then evaluate on held-out testing data. • Transductive SVM: • Learn hyperplane in the presence of labeled training data AND unlabeled testing data. Use distribution of testing points to assist you. • Easier to learn particular labels than a whole function. • More expensive than inductive

19. Transductive vs. Inductive SVM [Joachims ’99, ‘03]

20. Domain

21. Data • Notice difference in: • Length and density of protein names • Number of training examples: ||UT|| ~ 4*||Yapex|| • % positive examples: twice as many in Yapex <Protname>p35</Protname>/<Protname>cdk5 </Protname> binds and phosphorylates <Protname>beta-catenin</Protname> and regulates <Protname>beta-catenin </Protname> / <Protname>presenilin-1</Protname> interaction. <prot> p38 stress-activated proteinkinase </prot> inhibitor reverses <prot> bradykinin B(1) receptor </prot>-mediated component of inflammatory hyperalgesia.

22. Experiment • Examining three dimensions: • Labeled vs unlabeled vs prior auxiliary data • eg. % target positive examples, few labeled target data • Transduction vs induction • Transfer vs non-transfer • Since few true positives, focused on: F1 := (2 * Precision * Recall) / (Precision + Recall) • Source = UT, target = Yapex • For IPL, θ = .95 (conservative)

23. Results: Transfer • Transfer is much more difficult • Accuracy is not the problem

24. Results: Transduction • Transduction helps in transfer setting • TSVM copes better than MaxEnt, ISVM

25. Results: IPL • IPL can help boost performance • Makes transfer MaxEnt competitive with TSVM • But bounded by quality of initial pseudo-labels

26. Results: Priors • Priors improve unsupervised transfer • Threshold helps balance recall and precision  better F1 • A little bit of knowledge can help a lot

27. Results: Supervision • Supervised transfer beats supervised non-transfer • Significant at 99% binomial CI on precision and recall • But not by as much as might be hoped for • Even relatively simple transfer methods can help

28. Conclusions & Contributions • Introduced novel MaxEnt transfer method: IPL • Can match transduction in unsupervised setting • Gives probabilistic results • Analyzed and compared various methods related to transfer learning and concluded: • Transfer is hard • But made easier when explicitly addressed • Transduction is a good start • TSVM excels even with scant prior knowledge • A little prior target knowledge is even better • No need for fully labeled target data set

29. Limitations & Future Work • Threshold is important: • Currently only using at test time • Why not incorporate earlier, get better pseudo labels • Priors seem to help a lot: • Currently only using feature means, what about variances? • Can structuring feature space lead to parsimonious transferable priors? token left right token.is.capitalized token.is.numeric

30. Limitations & Future Work: high-level • How to better make use of source data? • Why doesn’t source data help so much? • Is IPL convex? • Is this exactly what we want to optimize? • How does regularization affect convexity? • What, exactly, is the relationship between transduction and transfer? • Can their theories be unified? • When is it worth explicitly modeling transfer? • How different do the domains need to be? • How much source/target data do we need? • What kind of priors do we need?

31. ☺ Thank you! ☺ ¿ Questions ?

32. References