Ph.D. Alumni
Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks 2011
Tuyen N. Huynh, PhD Thesis, Department of Computer Science, University of Texas at Austin.
159 pages.
Online Max-Margin Weight Learning for Markov Logic Networks 2011
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the Eleventh SIAM International Conference on Data Mining (SDM11), pp. 642--651, Mesa, Arizona, USA, April 2011.
Online Structure Learning for Markov Logic Networks 2011
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), Vol. 2, pp. 81-96, September 2011.
Online Max-Margin Weight Learning with Markov Logic Networks 2010
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), pp. 32--37, Atlanta, GA, July 2010.
Discriminative Learning with Markov Logic Networks 2009
Tuyen N. Huynh, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part 1, pp. 564--579, Bled, Slovenia, September 2009.
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the International Workshop on Statistical Relational Learning (SRL-09), Leuven, Belgium, July 2009.
Discriminative Structure and Parameter Learning for Markov Logic Networks 2008
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
Mapping and Revising Markov Logic Networks for Transfer Learning 2007
Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Mooney, In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07), pp. 608-614, Vancouver, BC, July 2007.
Formerly affiliated with Machine Learning