Bo Thiesson
RESEARCHER
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Research Interests
I am interested in learning from data. My work is primarily inspired by the fields of statistics, machine learning, data mining, and artificial intelligence. My recent work has focused on spatial statistics, query suggestion for search, online advertising, spam filtering, time-series analysis, scalable learning algorithms, boosting, personalization for handwriting recognition, clustering and mixture modeling, video-tooning, and smart text input methods. I have a long standing interest in model selection, Bayesian networks, as well as graphical models in general.
Publications
- Chong Wang, Bo Thiesson, Christopher Meek, and David Blei, Markov Topic Models, in D. van Dyk and M. Welling (Eds.), Proceedings of The Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, JMLR: W&CP 5, Journal of Machine Learning Research, April 2009
- Guy Shani, Christopher Meek, Tim Paek, Bo Thiesson, and Gina Danielle Venolia, Searching large indexes on tiny devices: Optimizing binary search with character pinning, in Proc. IUI 2009, Association for Computing Machinery, Inc., February 2009
- Tim Paek, Bo Thiesson, Y. C. Ju, and Bongshin Lee, Search Vox: Leveraging multimodal refinement and partial knowledge for mobile voice search, in Proceedings of User Interface Software and Technology (UIST), Association for Computing Machinery, Inc., October 2008
- Jingrui He and Bo Thiesson, Asymmetric Gradient Boosting with Application to Spam Filtering, in Fourth Conference on Email and Anti-Spam, CEAS, August 2007
- Kenneth Church and Bo Thiesson, The Wild Thing Goes Local, in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, Association for Computing Machinery, Inc., July 2007
- Kenneth Church, Bo Thiesson, and Robert Ragno, K-Best Suffix Arrays, in Proceedings of NAACL HLT 2007, Companion Volume, pages 17–20. Association for Computational Linguistic, Association for Computational Linguistics, April 2007
- Bo Thiesson and Jesper Lind, Mining cross-predicting stochastic ARMA time series in SQL server 2005, in Data Mining VII: Data, Text and Web Mining and their Business Applications. Information and Communication Technologies, WIT Press, July 2006
- Church, Ken, Thiesson, and Bo, The Wild Thing, in Proceedings of the ACL Interactive Poster and Demonstration Sessions, Association for Computational Linguistics, Ann Arbor, Michigan, June 2005
- Bo Thiesson and Christopher Meek, Efficient gradient computation for conditional Gaussian models, in Proceedings of Tenth International Workshop on Artificial Intelligence and Statistics, The Society for Artificial Intelligence and Statistics, January 2005
- Bo Thiesson, David Maxwell Chickering, David Heckerman, and Christopher Meek, ARMA Time-Series Modeling with Graphical Models, in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, AUAI Press, July 2004
- Jue Wang, Bo Thiesson, Yingqing Xu, and Michael Cohen, Image and Video Segmentation by Anisotropic Kernel Mean Shift, in Proceedings of European Conference on Computer Vision, Springer-Verlag, May 2004
- Bo Thiesson and Christopher Meek, Discriminative Model Selection for Density Models, in Proceedings of Ninth International Workshop on Artificial Intelligence and Statistics, The Society for Artificial Intelligence and Statistics, January 2003
- Christopher Meek, Bo Thiesson, and David Heckerman, Staged Mixture Modeling and Boosting, in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, August 2002
- Christopher Meek, Bo Thiesson, and David Heckerman, The Learning-Curve Sampling Method Applied to Model-Based Clustering, in Journal of Machine Learning Research, vol. 2, pp. 397-418, Journal of Machine Learning Research, February 2001
- Christopher Meek, Bo Thiesson, and David Heckerman, The learning-curve method applied to clustering, in Proceedings of Eighth International Workshop on Artificial Intelligence and Statistics, Morgan Kaufmann Publishers, January 2001
- Bo Thiesson, Christopher Meek, and David Heckerman, Accelerating EM for large databases, in Machine Learning, vol. 45, pp. 279-299, Kluwer Academic, January 2001
- David Maxwell Chickering, David Heckerman, Christopher Meek, John C. Platt, and Bo Thiesson, Goal-oriented clustering, no. MSR-TR-2000-82, May 2000
- Bo Thiesson, Christopher Meek, David Maxwell Chickering, and David Heckerman, Computationally efficient methods for selecting among mixtures of graphical models, with discussion, in Bayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting, pp. 631-656, Oxford University Press, May 1999
- Bo Thiesson, Christopher Meek, David Maxwell Chickering, and David Heckerman, Learning mixtures of DAG models, in Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, August 1998
- Bo Thiesson, Score and Information for Recursive Exponential Models with Incomplete Data, in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, August 1997
Downloads (access through my internal site)
- The WildThing
- Double-Joy
