A case study on meta-generalising: A Gaussian processes approach

Journal of Machine Learning Research

Volumn 13, Issue , 2012, Pages 691-721

  Skolidis, Grigorios ^a   Sanguinetti, Guido ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Gaussian processes; Meta generalising; Mixture of experts; Multi task learning; Transfer learning

Indexed keywords

GAUSSIAN PROCESSES; META-GENERALISING; MIXTURE OF EXPERTS; MULTITASK LEARNING; TRANSFER LEARNING;

GAUSSIAN DISTRIBUTION;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84859465410     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (52)

1. J. H. Albert and S. Chib. Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422):669-679, 1993.
2. R.K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. The Journal of Machine Learning Research, 6:1817-1853, 2005.
3. A. Argyriou, T. Evgeniou, and M. Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.
4. A. Arnold, R. Nallapati, and W.W. Cohen. A comparative study of methods for transductive transfer learning. In Proceedings of the 7th IEEE International Conference on Data Mining Workshops, pages 77-82, Omaha, Nebraska, USA, 2007.
5. B. Bakker and T. Heskes. Task clustering and gating for bayesian multitask learning. The Journal of Machine Learning Research, 4:83-99, 2003.
6. J. Baxter. A model of inductive bias learning. Journal of Artificial Intelligence Research, 12: 149-198, 2000.
7. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In Advances in Neural Information Processing Systems 19, pages 137-145, Vancouver, Canada, 2007.
8. S. Ben-David, T. Luu, T. Lu, and D. Pál. Impossibility theorems for domain adaptation. In Proceedings of the 13th International Workshop on Artificial Intelligence and Statistics, volume 13, pages 129-136, Sardinia, Italy, 2010.
9. S. Bickel, M. Brückner, and T. Scheffer. Discriminative learning under covariate shift. The Journal of Machine Learning Research, 10:2137-2155, 2009.
10. E. Bonilla, K. M. Chai, and C.K.I. Williams. Multi-task gaussian process prediction. In Advances in Neural Information Processing Systems 20, pages 153-160, Vancouver, Canada, 2008.
11. K.H. Brodersen, C.S. Ong, K.E. Stephan, and J.M. Buhmann. The binormal assumption on precision-recall curves. In Proceedings of the 2010 International Conference on Pattern Recognition, pages 4263-4266, Istanbul, Turkey, 2010.
12. R. Caruana. Multi-task learning. Machine Learning, 28(1):41-75, 1997.
13. Chapelle, B. Schölkopf, and A. Zien. Semi-supervised learning. MIT Press, Cambridge, MA, 2006.
14. K. Crammer, M. Kearns, and J. Wortman. Learning from multiple sources. The Journal of Machine Learning Research, 9:1757-1774, 2008.
15. N. A.C. Cressie. Statistics for Spatial Data. John Wiley & Sons. New York. US, 1993.
16. L. Csató, E. Fokoué, M. Opper, B. Schottky, and O. Winther. Efficient approaches to gaussian process classification. In Advances in Neural Information Processing Systems 12, pages 251-257, Denver, Colorado, 2000.
17. H. Daumé. Frustratingly easy domain adaptation. In Annual Meeting of the Association for Computational Linguistics, volume 45, pages 256-263, 2007.
18. H. Daumé III. Bayesian multitask learning with latent hierarchies. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pages 135-142, Montreal, Canada, 2009.
19. H. Daumé III and D. Marcu. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26(1):101-126, 2006. (Pubitemid 44452208)
20. J. Davis and M. Goadrich. The relationship between precision-recall and roc curves. In Proceedings of the 23rd International Conference on Machine Learning, pages 233-240, Pittsburgh, USA, 2006.
21. I. Deak. Three digit accurate multiple normal probabilities. Numerische Mathematik, 35(4):369-380, 1980.
22. H. I. Gassmann, I. Deak, and T. Szantai. Computing multivariate normal probabilities: A new look. Journal of Computational and Graphical Statistics, 11(4):920-949, 2002. (Pubitemid 36047114)
23. A. Genz. Numerical computation of multivariate normal probabilities. Journal of Computational and Graphical Statistics, 1(2):141-149, 1992.
24. M. Girolami and S. Rogers. Variational bayesian multinomial probit regression with gaussian process priors. Neural Computation, 18(8):1790-1817, 2006. (Pubitemid 44036395)
25. M. Girolami and M. Zhong. Data integration for classification problems employing Gaussian process priors. In Advances in Neural Information Processing Systems 19, pages 465-472, Vancouver, Canada, 2007.
26. A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley. Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation, 101(23): 215-220, 2000.
27. A. K. Gupta and D. K. Nagar. Matrix Variate Distributions. Chapman & Hall/CRC, 2000.
28. J. A. Hanley and B. J. Mcneil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1):29-36, April 1982. (Pubitemid 12142173)
29. G.E. Hinton. Connectionist learning procedures. Artificial Intelligence, 40(1-3):185-234, 1989.
30. J. Huang, A. J. Smola, A. Gretton, K M. Borgwardt, and B. Schölkopf. Correcting sample selection bias by unlabeled data. In Advances in Neural Information Processing Systems 19, pages 601-608, Vancouver, Canada, 2007.
31. R.A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3(1):79-87, 1991.
32. L.I. Kuncheva. A theoretical study on six classifier fusion strategies. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2):281-286, 2002. (Pubitemid 34198215)
33. Q. Liu, X. Liao, H. Li, J. R. Stack, and L. Carin. Semisupervised multitask learning. IEEE Transactions on Pattern Analysis Machine Intelligence, 31(6):1074-1086, 2009.
34. D.J.C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
35. Y. Mansour, M. Mohri, and A. Rostamizadeh. Domain adaptation with multiple sources. In Advances in Neural Information Processing Systems 21, pages 1041-1048, Vancouver, Canada, 2009.
36. T.P. Minka. Expectation propagation for approximate bayesian inference. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, volume 17, pages 362-369, San Francisco, CA, USA, 2001.
37. M. Opper and O. Winther. Gaussian processes for classification: mean-field algorithms. Neural Computation, 12(11):2655-2684, 2000.
38. S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345-1359, 2010.
39. S.J. Pan, I.W. Tsang, J.T. Kwok, and Q. Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2):199-210, 2009.
40. R. Raina, A. Battle, H. Lee, B. Packer, and A.Y. Ng. Self-taught learning: Transfer learning from unlabeled data. In Proceedings of the 24th International Conference on Machine Learning, pages 759-766, Corvallis, OR, USA, 2007.
41. C. E. Rasmussen and C. K.I. Williams. Gaussian Processes for Machine Learning. MIT press, 2005.
42. C.E. Rasmussen and Z. Ghahramani. Infinite mixtures of gaussian process experts. In Advances in Neural Information Processing Systems 14, pages 881-888, Vancouver, Canada, 2001.
43. R. Rebonato and P. Jäckel. The most general methodology to create a valid correlation matrix for risk management and option pricing purposes. Journal of Risk, 2(2), 2000.
44. G. Skolidis and G. Sanguinetti. Bayesian multitask classification with gaussian process priors. IEEE Transactions on Neural Networks, 22(12):2011-2021, Dec. 2011.
45. G. Skolidis, RH Clayton, and G. Sanguinetti. Automatic classification of arrhythmic beats using gaussian processes. In IEEE Transactions on Computers in Cardiology, 2008, pages 921-924, Bologna, Italy, 2008.
46. E. Snelson and Z. Ghahramani. Sparse gaussian processes using pseudo-inputs. In Advances in Neural Information Processing Systems 18, pages 1257-1264, Vancouver, Canada, 2006.
47. A. J. Storkey and M. Sugiyama. Mixture regression for covariate shift. In Advances in Neural Information Processing Systems 19, pages 1337-1344, Vancouver, Canada, 2007.
48. M. Sugiyama, M. Krauledat, and K.R. Müller. Covariate shift adaptation by importance weighted cross validation. The Journal of Machine Learning Research, 8:985-1005, 2007. (Pubitemid 46798411)
49. Volker Tresp. Mixtures of gaussian processes. In Advances in Neural Information Processing Systems 13, pages 654-660, Vancouver, Canada, 2000. MIT Press.
50. S.R. Waterhouse. Classification and Regression Using Mixtures of Experts. PhD thesis, Department of Engineering, Cambridge University, 1997.
51. Y. Xue, X. Liao, L. Carin, and B. Krishnapuram. Multi-task learning for classification with dirichlet process priors. The Journal of Machine Learning Research, 8:35-63, 2007. (Pubitemid 46155123)
52. K. Yu, V. Tresp, and A. Schwaighofer. Learning gaussian processes from multiple tasks. In Proceedings of the 22nd International Conference on Machine Learning, pages 1012-1019, Bonn, Germany, 2005.