Domain adaptation: Learning bounds and algorithms

COLT 2009 - The 22nd Conference on Learning Theory

Volumn , Issue , 2009, Pages

  Mansour, Yishay ^a   Mohri, Mehryar ^b   Rostamizadeh, Afshin ^c  

^a TEL AVIV UNIVERSITY   (Israel)

^b GOOGLE INC   (United States)

^c NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DOMAIN ADAPTATION; FINITE SAMPLES; GENERALIZATION BOUND; KERNEL RIDGE REGRESSIONS; LOSS FUNCTIONS; MINIMIZATION ALGORITHMS; RADEMACHER COMPLEXITY; TEST DATA;

REGRESSION ANALYSIS; SAMPLING;

ALGORITHMS;

EID: 84898072330     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. Alizadeh, F. (1995). Interior point methods in semidefinite programming with applications to combinatorial optimization. SIAM Journal on Optimization, 5, 13-51.
2. Bartlett, P. L., & Mendelson, S. (2002). Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3, 2002.
3. Ben-David, S., Blitzer, J., Crammer, K., & Pereira, F. (2007). Analysis of representations for domain adaptation. Proceedings of NIPS 2006.
4. Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Wortman, J. (2008). Learning bounds for domain adaptation. Proceedings of NIPS 2007.
5. Blitzer, J., Dredze, M., & Pereira, F (2007). Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. ACL 2007.
6. Bousquet, O., & Elisseeff, A. (2002). Stability and generalization. JMLR, 2, 499-526.
7. Chazelle, B. (2000). The discrepancy method: randomness and complexity. New York: Cambridge University Press.
8. Chelba, C, & Acero, A. (2006). Adaptation of maximum entropy capitalizer: Little data can help a lot. Computer Speech & Language, 20, 382-399.
9. Cortes, C, Mohri, M., Riley, M., & Rostamizadeh, A. (2008). Sample selection bias correction theory. Proceedings of ALT 2008. Springer, Heidelberg, Germany.
10. Cortes, C, & Vapnik, V (1995). Support-Vector Networks. Machine Learning, 20.
11. Daumé III, H., & Marcu, D. (2006). Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26, 101-126.
12. Devroye, L., Györfi, L., & Lugosi, G. (1996). A probabilistic theory of pattern recognition. Springer.
13. Dredze, M., Blitzer, J., Talukdar, P. P., Ganchev, K., Graca, J., & Pereira, F (2007). Frustratingly Hard Domain Adaptation for Parsing. CoNLL 2007.
14. Elkan, C. (2001). The foundations of cost-sensitive learning. IJCAI (pp. 973-978).
15. Fletcher, R. (1985). On minimizing the maximum eigenvalue of a symmetric matrix. SIAMJ. Control and Optimization, 23, 493-513.
16. Gauvain, J.-L., & Chin-Hui (1994). Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains. IEEE Transactions on Speech and Audio Processing, 2, 291-298.
17. Helmberg, C, & Oustry, F (2000). Bundle methods to minimize the maximum eigenvalue function. In Handbook of semidefinite programming: Theory, algorithms, and applications. Kluwer Academic Publishers, Boston, MA.
18. Jarre, F (1993). An interior-point method for minimizing the maximum eigenvalue of a linear combination of matrices. SIAMJ. Control Optim., 31, 1360-1377.
19. Jelinek, F (1998). Statistical Methods for Speech Recognition. The MIT Press.
20. Jiang, J., & Zhai, C. (2007). Instance Weighting for Domain Adaptation in NLP. Proceedings of ACL 2007 (pp. 264-271). Association for Computational Linguistics.
21. Karabati, S., Kouvelis, P., & Yu, G. (2001). A min-max-sum resource allocation problem and its application. Operations Research, 49, 913-922.
22. Kifer, D., Ben-David, S., & Gehrke, J. (2004). Detecting change in data streams. Proceedings of the 30th International Conference on Very Large Data Bases.
23. Koltchinskii, V., & Panchenko, D. (2000). Rademacher processes and bounding the risk of function learning. In High dimensional probability ii, 443-459. preprint.
24. Ledoux, M., & Talagrand, M. (1991). Probability in Banach spaces: isoperimetry and processes. Springer.
25. Legetter, C. J., & Woodland, P. C. (1995). Maximum likelihood linear regression for speaker adaptation of continuous density hidden markov models. Computer Speech and Language, 171-185.
26. Mansour, Y., Mohri, M., & Rostamizadeh, A. (2009). Domain adaptation with multiple sources. Advances in Neural Information Processing Systems (2008).
27. Martínez, A. M. (2002). Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell., 24, 748-763.
28. Nesterov, Y., & Nemirovsky, A. (1994). Interior point polynomial methods in convex programming: Theory and applications. SIAM.
29. Overton, M. L. (1988). On minimizing the maximum eigenvalue of a symmetric matrix. SIAM J. Matrix Anal. Appl., 9, 256-268.
30. Pietra, S. D., and Pietra, V. D., Mercer, R. L., & Roukos, S. (1992). Adaptive language modeling using minimum discriminant estimation. HLT'91: Proceedings of the workshop on Speech and Natural Language (pp. 103-106).
31. Roark, B., & Bacchiani, M. (2003). Supervised and unsu-pervised PCFG adaptation to novel domains. Proceedings of HLT-NAACL.
32. Rosenfeld, R. (1996). A Maximum Entropy Approach to Adaptive Statistical Language Modeling. Computer Speech and Language, 10, 187-228.
33. Saunders, C., Gammerman, A., & Vovk, V. (1998). Ridge Regression Learning Algorithm in Dual Variables. ICML (pp. 515-521).
34. Valiant, L. G. (1984). A theory of the learnable. ACM Press New York, NY, USA.
35. Vapnik, V. N. (1998). Statistical learning theory. John Wiley & Sons.