Max-margin nonparametric latent feature models for link prediction

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 719-726

  Zhu, Jun ^a  

^a TSINGHUA NATIONAL LABORATORY FOR INFORMATION SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NONPARAMETRICS; EXPECTATION OPERATOR; FEATURE MODELS; HIGHLY NONLINEAR; LIKELIHOOD FUNCTIONS; LINK PREDICTION; NON-PARAMETRIC; NONPARAMETRIC INFERENCE; REAL DATA SETS; RELATIONAL MODEL; SOCIAL DIMENSIONS;

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

LEARNING SYSTEMS;

EID: 84867115033     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Airoldi, E., Blei, D.M., Fienberg, S.E., and Xing, E.P. Mixed membership stochastic blockmodels. In NIPS, pp. 33-40, 2008.
2. Akbani, R., Kwek, S., and Japkowicz, N. Applying support vector machines to imbalanced datasets. In ECML, 2004.
3. Backstrom, L. and Leskovec, J. Supervised random walks: predicting and recommending links in social networks. In WSDM, 2011.
4. Chapelle, O., Vapnik, V., Bousquet, O., and Mukherjee, S. Choosing multiple parameters for support vector machines. Machine Learning, 46:131-159, 2002.
5. Doshi-Velez, F., Miller, K., Gael, J. Van, and Teh, Y.W. Variational inference for the Indian buffet process. In AISTATS, 2009.
6. Gold, C., Holub, A., and Sollich, P. Bayesian approach to feature selection and parameter tuning for support vector machine classifiers. Neural Networks, 18(5-6): 693-701, 2005.
7. Griffin, J.E. and Brown, P.J. Inference with normal-gamma prior distributions in regression problems. Bayesian Analysis, 5(1):171-188, 2010.
8. Griffiths, T.L. and Ghahramani, Z. Infinite latent feature models and the Indian buffet process. In NIPS, 2006.
9. Hoff, P., Raftery, A., and Handcock, M. Latent space approaches to social network analysis. Journal of American Statistical Association, 97(460), 2002.
10. Hoff, P.D. Modeling homophily and stochastic equivalence in symmetric relational data. In NIPS, 2007.
11. Jaakkola, T., Meila, M., and Jebara, T. Maximum entropy discrimination. In NIPS, 1999.
12. Jebara, T. Discriminative, generative and imitative learning. PhD Thesis, 2002.
13. Kemp, C., Tenenbaum, J., Griffithms, T., Yamada, T., and Ueda, N. Learning systems of concepts with an infinite relational model. In AAAI, 2006.
14. Lewis, D.P., Jebara, T., and Noble, W.S. Nonstationary kernel combination. In ICML, 2006.
15. Liben-Nowell, D. and Kleinberg, J.M. The link prediction problem for social networks. In CIKM, 2003.
16. Meeds, E., Ghahramani, Z., Neal, R., and Roweis, S. Modeling dyadic data with binary latent factors. In NIPS, 2007.
17. Miller, K., Griffiths, T., and Jordan, M. Nonparametric latent feature models for link prediction. In NIPS, 2009.
18. Nowicki, K. and Snijders, T. A. B. Estimation and prediction for stochastic blockstructures. Journal of American Statistical Association, 96(455), 2001.
19. Teh, Y.W., Gorur, D., and Ghahramani, Z. Stick-breaking construction of the Indian buffet process. In AISTATS, 2007.
20. Zhu, J. and Xing, E.P. Maximum entropy discrimination markov networks. JMLR, 10:2531-2569, 2009.
21. Zhu, J., Ahmed, A., and Xing, E.P. MedLDA: Maximum margin supervised topic models for regression and classification. In ICML, 2009.
22. Zhu, J., Chen, N., and Xing, E.P. Infinite SVM: a Dirichlet process mixture of large-margin kernel machines. In ICML, 2011a.
23. Zhu, J., Chen, N., and Xing, E.P. Infinite latent SVM for classification and multi-task learning. In NIPS, 2011b.