nips nips2006 nips2006-183 nips2006-183-reference knowledge-graph by maker-knowledge-mining
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Author: Kai Yu, Wei Chu, Shipeng Yu, Volker Tresp, Zhao Xu
Abstract: We introduce a Gaussian process (GP) framework, stochastic relational models (SRM), for learning social, physical, and other relational phenomena where interactions between entities are observed. The key idea is to model the stochastic structure of entity relationships (i.e., links) via a tensor interaction of multiple GPs, each defined on one type of entities. These models in fact define a set of nonparametric priors on infinite dimensional tensor matrices, where each element represents a relationship between a tuple of entities. By maximizing the marginalized likelihood, information is exchanged between the participating GPs through the entire relational network, so that the dependency structure of links is messaged to the dependency of entities, reflected by the adapted GP kernels. The framework offers a discriminative approach to link prediction, namely, predicting the existences, strengths, or types of relationships based on the partially observed linkage network as well as the attributes of entities (if given). We discuss properties and variants of SRM and derive an efficient learning algorithm. Very encouraging experimental results are achieved on a toy problem and a user-movie preference link prediction task. In the end we discuss extensions of SRM to general relational learning tasks. 1
[1] J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In Proceedings of the 21st International Conference on Machine Learning (ICML), 2004.
[2] E. V. Bonilla, F. V. Agakov, and C. K. I. Williams. Kernel multi-task learning using task-specific features. In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS), 2007. To appear.
[3] J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI), 1998.
[4] W. Chu, V. Sindhwani, Z. Ghahramani, and S. S. Keerthi. Relational learning with gaussian processes. In Neural Information Processing Systems (NIPS), 2007. To appear.
[5] L. Getoor, E. Segal, B. Taskar, and D. Koller. Probabilistic models of text and link structure for hypertext classification. In Proceedings ICJAI Workshop on Text Learning: Beyond Supervision, 2001.
[6] Arjun K. Gupta and Daya K. Naga. Matrix Variate Distributions. 1999.
[7] C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada, and N. Ueda. Learning systems of concepts with an infinite relational model. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI), 2006.
[8] D. Koller and A. Pfeffer. Probabilistic frame-based systems. In Proceedings of National Conference on Artificial Intelligence (AAAI), 1998.
[9] C. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
[10] Jason D. M. Rennie and Nati Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22nd International Conference on Machine Learning (ICML), 2005.
[11] B. Taskar, M. F. Wong, P. Abbeel, and D. Koller. Link prediction in relational data. In Neural Information Processing Systems Conference (NIPS), 2004.
[12] Z. Xu, V. Tresp, K. Yu, and H.-P. Kriegel. Infinite hidden relational models. In Proceedings of the 22nd International Conference on Uncertainty in Artificial Intelligence (UAI), 2006.
[13] K. Yu, V. Tresp, and A. Schwaighofer. Learning Gaussian processes from multiple tasks. In Proceedings of 22nd International Conference on Machine Learning (ICML), 2005.