I am a manager in the Machine Learning Department in Microsoft Research.

# Research Activities

I am primarily interested in statistics and machine learning. My work has touched a large number of areas including the analysis of sequence data (e.g., web logs), probabilistic models for relational data, auction design, scalable algorithms, computational biology, data mining, collaborative filtering, recommendation systems, text classification, bioinformatics, clustering and mixture models. I have a long standing interest in model selection, learning and using graphical models, and learning causal relationships from non-randomized studies.

My research contributions have had an impact on a large number of systems/products during my tenure at Microsoft including Microsoft SQL Analysis Services (data mining), Microsoft AdCenter, Microsoft Bing, Microsoft Dynamics Live, Windows Tablet PC (handwriting recognition), Microsoft Commerce Server (recommender system).

I am an affiliate professor at the University of Washington. I am also an associate editor for the Journal of Machine Learning Research and for Statistics and Computing and was previously an associate editor for the Journal of Artificial Intelligence Research. I was the program chair for Uncertainty and Artificial Intelligence (UAI) in 2003 and the general chair for UAI in 2004.

## Selected papers grouped by topic.

## Graphical Models

## Machine Learning

## E-Commerce

## Computational Biology

## Other papers and bibtex entries

- Dan Geiger, Christopher Meek, and Bernd Sturmfels, On the Toric Algebra of Graphical Models, in
*The Annals of Statistics*, vol. 34, no. 3, pp. 1463-1492, 2006. - D. Geiger, D. Heckerman, H. King, and C. Meek, Stratified exponential families: graphical models and model selection, in
*Annals of Statistics*, vol. 29, pp. 505-529, 2001. - D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie, Dependency Networks for Inference, Collaborative Filtering, and Data Visualization, in
*Journal of Machine Learning Research*, vol. 1, no. MSR-TR-2000-16, pp. 49-75, Journal of Machine Learning Research, October 2000. - David Heckerman, Christopher Meek, and Daphne Koller, Probabilistic Entity-Relationship Models, PRMs and Plate Models, in Introduction to Statistical Relational Learning, pp. 201-239, MIT Press, 2007.

- David Maxwell Chickering, David Heckerman, and Christopher Meek, Large-Sample Learning of Bayesian Networks is NP-Hard, in
*Journal of Machine Learning Research*, vol. 5, pp. 1287-1330, 2004. - Christopher Meek, Finding a Path is Harder than Finding a Tree, in
*Journal of Artificial Intelligence Research*, vol. 15, pp. 383-389, 2001. - D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie, Dependency Networks for Inference, Collaborative Filtering, and Data Visualization, in
*Journal of Machine Learning Research*, vol. 1, no. MSR-TR-2000-16, pp. 49-75, Journal of Machine Learning Research, October 2000. - Max Chickering and Christopher Meek, On the incompatibility of faithfulness and monotone-DAG-faithfulness, in
*Artificial Intelligence*, vol. 170, no. 8-9, pp. 653-666, 2006. - D. Geiger, D. Heckerman, and C. Meek, Asymptotic model selection for directed networks with hidden variables, in Proceedings of Twelfth Conference on Uncertainty in Artificial Intelligence, ® Portland, OR, pp. 283-290, Morgan Kaufmann, August 1996.
- B. Thiesson, C. Meek, D.M. Chickering, and D. Heckerman, Computationally Efficient Methods for Selecting Among Mixtures of Graphical Models, in Bayesian Statistics 6, pp. 631-656, Oxford University Press, May 1999.
- D.M. Chickering, D. Heckerman, and C. Meek, A Bayesian approach to learning Bayesian networks with local structure, in Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, ® Providence, RI, no. MSR-TR-97-07, pp. 80-89, Morgan Kaufmann, August 1997.
- David Maxwell Chickering and Christopher Meek, Finding Optimal Bayesian Networks, in
*Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, ® Alberta, Edmonton*, Morgan Kaufmann, July 2002. - C. Meek, Strong completeness and faithfulness in Bayesian networks, in Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, ® Montreal, QU, pp. 411-418, Morgan Kaufmann, August 1995.

- Dan Geiger and Christopher Meek, Quantifier Elimination for statistical problems, in
*Proceedings of Fifteenth Conference on Uncertainty in Artificial Intelligence, ® Stockholm, Sweden*, Morgan Kaufmann, August 1999. - Dan Geiger, Christopher Meek, and Bernd Sturmfels, On the Toric Algebra of Graphical Models, in
*The Annals of Statistics*, vol. 34, no. 3, pp. 1463-1492, 2006. - D. Geiger, D. Heckerman, H. King, and C. Meek, Stratified exponential families: graphical models and model selection, in
*Annals of Statistics*, vol. 29, pp. 505-529, 2001.

- Christopher Meek and Clark Glymour, Conditioning and Intervening, in
*British Journal for the Philosophy of Science*, vol. 45, pp. 1001-1021, 1994. - C. Meek, Graphical models: selecting causal and statistical models, Pittsburgh, PA, 1997.
- Peter Spirtes, Thomas Richardson, Christopher Meek, Richard Scheines, and Clark Glymour, Using path diagrams as a structural equation modeling tool, in
*Sociological Methods and Research*, vol. 27, no. 2, pp. 182-226, 1998. - D. Heckerman, C. Meek, and G. Cooper, A Bayesian approach to causal discovery, in Computation, Causation, and Discovery, pp. 141-166, AAAI Press, Menlo Park, CA, 1999.
- P. Spirtes, C. Meek, and T. Richardson, An Algorithm for causal inference in the presence of latent variables and selection bias, in Computation, Causation, and Discovery, pp. 211-252, AAAI Press, Menlo Park, CA, 1999.
- C. Meek, Causal inference and causal explanation with background knowledge, in Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, ® Montreal, QU, pp. 403-418, Morgan Kaufmann, August 1995.
- C. Meek, Strong completeness and faithfulness in Bayesian networks, in Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, ® Montreal, QU, pp. 411-418, Morgan Kaufmann, August 1995.
- C. Meek and D. Heckerman, Structure and Parameter Learning for Causal Independence and Causal Interaction Models, in Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, ® Providence, RI, Morgan Kaufmann, August 1997.
- P. Spirtes, C. Glymour, R. Scheines, C. Meek, S. Fienberg, and E. Slate, Prediction and experimental design with Graphical causal models, in Computation, Causation, and Discovery, pp. 65-94, AAAI Press, Menlo Park, CA, 1999.

- Christopher Meek and Ydo Wexler, Improved Approximate Sum-Product Inference Using Multiplicative Error Bounds, in Bayesian Statistics 9, Oxford University Press, August 2011.
- Dan Geiger, Christopher Meek, and Ydo Wexler, A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints, in
*Journal of Artificial Intelligence Research*, vol. 27, pp. 1-23, 2006. - Dan Geiger, Christopher Meek, and Ydo Wexler, Speeding up HMM algorithms for genetic linkage analysis, in
*Bioinformatics*, Oxford University Press, 2009. - Ydo Wexler and Christopher Meek, Inference for Multiplicative Models, in
*Proceedings of Uncertainty in Artificial Intelligence*, 2008. - Ydo Wexler and Christopher Meek, MAS: a multiplicative approximation scheme for probabilistic inference, in
*NIPS*, 2008.

- Asela Gunawardana and Christopher Meek, Tied Boltzmann Machines for Cold Start Recommendations, in
*ACM International Conference on Recommender Systems*, Association for Computing Machinery, Inc., October 2008. - Asela Gunawardana and Christopher Meek, A Unified Approach to Building Hybrid Recommmender Systems, in
*ACM International Conference on Recommender Systems*, Association for Computing Machinery, Inc., October 2009. - D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie, Dependency Networks for Density Estimation, Collaborative Filtering, and Data Visualization, in Proceedings of Sixteenth Conference on Uncertainty in Artificial Intelligence, ® Stanford, CA, pp. 82-88, Morgan Kaufmann Publishers, 2000.
- Andrew Zimdars, David Maxwell Chickering, and Christopher Meek, Using Temporal Data for Making Recommendations, in
*Proceedings of Seventeenth Conference on Uncertainty in Artificial Intelligence, ® Seattle, WA*, Morgan Kaufmann Publishers, 2001.

- Asela Gunawardana, Christopher Meek, and Puyang Xu, A Model for Temporal Dependencies in Event Streams, in
*Neural Information Processing Systems*, Neural Information Processing Systems Foundation, December 2011. - Guy Shani, Asela Gunawardana, and Christopher Meek, Unsupervised hierarchical probabilistic segmentation of discrete events, in
*Intelligent Data Analysis*, IOS Press, 27 June 2011. - 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. - Christopher Meek, David Maxwell Chickering, and David Heckerman, Autoregressive tree models for time-series analysis, in
*Proceedings of the Second International SIAM Conference on Data Mining*, SIAM, Arlington, VA, April 2002. - Heikki Mannila and Christopher Meek, Global Partial Orders from Sequential Data, in
*Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ® Boston, MA*, ACM Press, 2000. - Guy Shani, Christopher Meek, and Asela Gunawardana, Hierarchical Probabilistic Segmentation of Discrete Events, in
*IEEE International Conference on Data Mining*, IEEE, 6 December 2009. - Igor Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, and Steven White, Model-Based Clustering and Visualization of Navigation Patterns on a Web Site, in
*Data Mining and Knowledge Discovery*, vol. 7, no. 4, pp. 399-424, 2003.

- Daniel Lowd and Christopher Meek, Adversarial learning, in
*KDD*, ACM – Association for Computing Machinery, August 2005. - Daniel Lowd and Christopher Meek, Good Word Attacks on Statistical Spam Filters, in Conference on email and anti-spam (CEAS), 2005.

- Bo Thiesson, Christopher Meek, and David Heckerman, Accelerating EM for large databases, in
*Machine Learning*, vol. 45, pp. 279-299, Kluwer Academic, January 2001. - 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.

- Ece Kumar, Eric Horvitz, and Chris Meek, Mobile Opportunistic Commerce: Mechanisms, Architecture, and Application, in
*AAMAS*, 2008. - Christopher Meek, David Maxwell Chickering, and David Wilson, Stochastic and contingent payment auctions, in Workshop on Sponsored Search Auctions, ACM Electronic Commerce, 2005.
- Asela Gunawardana and Christopher Meek, Aggregators and Contextual Effects in Search Ad Markets, in
*WWW Workshop on Targeting and Ranking for Online Advertising*, Association for Computing Machinery, Inc., April 2008. - Asela Gunawardana, Christopher Meek, and Jody Biggs, A Quality-Based Auction for Search Ad Markets with Aggregators, in
*ACM EC Workshop on Ad Auctions*, Association for Computing Machinery, Inc., June 2008. - Greg Linden, Christopher Meek, and Max Chickering, The Pollution Effect: Optimizing Keyword Auctions by Favoring Relevant Advertising, in
*Fifth workshop on Ad Auctions*, 6 July 2009.

- Jim C. Huang, Christopher Meek, Carl Kadie, and David Heckerman, Conditional Random Fields for Fast, Large-Scale Genome-Wide Association Studies, in
*PLoS ONE*, PLoS, 12 July 2011. - S. Bercovici, C. Meek, Y. Wexler, and D. Geiger, Estimating Genomewide IBD-sharing from SNP data via an efficient hidden Markov model of LD with application to gene mapping, in
*Bioinformatics*, vol. 26, no. 12, pp. 175-182, Oxford University Press, 2010. - Dan Geiger, Christopher Meek, and Ydo Wexler, Speeding up HMM algorithms for genetic linkage analysis, in
*Bioinformatics*, Oxford University Press, 2009. - Helge G. Gyllenberg, Mats Gyllenberg, Timo Koski, Tatu Lund, Heikki Mannila, and Christopher Meek, Singling out ill-fit items in a classification. Application to the taxonomy of Enterobacteriaceae, in
*Archive of Control Science*, vol. 9, pp. 97-107, 1999. - Nebojsa Jojic, Vladimir Jojic, Brendan J. Frey, Christopher Meek, and David Heckerman, Using epitomes to model genetic diversity: Rational design of HIV vaccines, in NIPS, 2005.
- Vladimir Jojic, Nebojsa Jojic, Christopher Meek, Dan Geiger, Adam Siepel, David Haussler, and David Heckerman, Efficient approximations for learning phylogenetic HMM models from data, in
*ISMB/ECCB (Supplement of Bioinformatics)*, pp. 161-168, 2004. - Gregory F. Cooper, Constantin F. Aliferis, R. Ambrosino, John M. Aronis, Bruce G. Buchanan, Rich Caruana, Michael J. Fine, Clark Glymour, G. Gordon, B. H. Hanusa, Janine E. Janosky, Christopher Meek, Tom M. Mitchell, Thomas Richardson, and Peter Spirtes, An evaluation of machine-learning methods for predicting pneumonia mortality, in
*Artificial Intelligence in Medicine*, vol. 9, no. 2, pp. 107-138, 1997.

## Contact Info

E-mail:meek@microsoft.com |

FAX: 425-936-7329 |

Mail: One Microsoft Way, 99/3118, Redmond WA 98052-6399, USA |

- Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh, The Value of Semantic Parse Labeling for Knowledge Base Question Answering, ACL – Association for Computational Linguistics, August 2016.
- Asela Gunawardana and Christopher Meek, Universal Models of Multivariate Temporal Point Processes , in
*Proceedings of the 19th International Conference on Artificial Intelligence and Statistics*, MIT Press, May 2016. - Yi Yang, Wen-tau Yih, and Christopher Meek, WikiQA: A Challenge Dataset for Open-Domain Question Answering, in
*Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*, ACL – Association for Computational Linguistics, 21 September 2015. - David Maxwell Chickering and Christopher Meek, Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations, no. MSR-TR-2015-45, June 2015.
- Helen J. Wang, Alexander Moshchuk, Michael Gamon, Mona Haraty, Shamsi Iqbal, Eli T. Brown, Ashish Kapoor, Chris Meek, Eric Chen, Yuan Tian, Jaime Teevan, Mary Czerwinski, and Susan Dumais, The Activity Platform, no. MSR-TR-2015-38, 8 May 2015.
- Kai-Wei Chang, Wen-tau Yih, Bishan Yang, and Christopher Meek, Typed Tensor Decomposition of Knowledge Bases for Relation Extraction, in
*Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing*, ACL – Association for Computational Linguistics, October 2014. - David Heckerman, Christopher Meek, and Thomas S. Richardson, Variations on Undirected Graphical Models and their Relationships, in
*Kybernetika*, Kybernetika, July 2014. - Christopher Meek, Toward Learning Graphical and Causal Process Models, July 2014.
- Wen-tau Yih, Xiaodong He, and Christopher Meek, Semantic Parsing for Single-Relation Question Answering, in
*Proceedings of ACL*, Association for Computational Linguistics, June 2014. - Dengyong Zhou, Qiang Liu, John C. Platt, and Christopher Meek, Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy, in
*Proceedings of the 31st International Conference on Machine Learning (ICML)*, June 2014. - Kai-Wei Chang, Wen-tau Yih, and Chris Meek, Multi-Relational Latent Semantic Analysis, in
*Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)*, ACL – Association for Computational Linguistics, October 2013. - Wen-tau Yih, Ming-Wei Chang, Christopher Meek, and Andrzej Pastusiak, Question Answering Using Enhanced Lexical Semantic Models, in
*Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics*, ACL – Association for Computational Linguistics, August 2013. - Alisa Zhila, Wen-tau Yih, Chris Meek, Geoffrey Zweig, and Tomas Mikolov, Combining Heterogeneous Models for Measuring Relational Similarity, in
*Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-2013)*, Association for Computational Linguistics, 27 May 2013. - Ankur P. Parikh, Asela Gunawardana, and Christopher Meek, Conjoint Modeling of Temporal Dependencies in Event Streams, in
*UAI Bayesian Modelling Applications Workshop*, 18 August 2012. - Geoffrey Zweig, John C. Platt, Christopher Meek, Christopher J.C. Burges, Ainur Yessenalina, and Qiang Liu, Computational Approaches to Sentence Completion, in
*ACL 2012*, ACL/SIGPARSE, July 2012. - Asela Gunawardana, Christopher Meek, and Puyang Xu, A Model for Temporal Dependencies in Event Streams, in
*Neural Information Processing Systems*, Neural Information Processing Systems Foundation, December 2011. - Christopher Meek and Ydo Wexler, Improved Approximate Sum-Product Inference Using Multiplicative Error Bounds, in Bayesian Statistics 9, Oxford University Press, August 2011.
- Jim C. Huang, Christopher Meek, Carl Kadie, and David Heckerman, Conditional Random Fields for Fast, Large-Scale Genome-Wide Association Studies, in
*PLoS ONE*, PLoS, 12 July 2011. - Guy Shani, Asela Gunawardana, and Christopher Meek, Unsupervised hierarchical probabilistic segmentation of discrete events, in
*Intelligent Data Analysis*, IOS Press, 27 June 2011. - Wen-tau Yih, Kristina Toutanova, John Platt, and Chris Meek, Learning Discriminative Projections for Text Similarity Measures, in
*Proceedings of the Fifteenth Conference on Computational Natural Language Learning*, Association for Computational Linguistics, 13 June 2011. - Jim Huang, Nebojsa Jojic, and Christopher Meek, Exact inference and learning for cumulative distribution functions on loopy graphs, in
*Advances in Neural Information Processing Systems 23*, MIT Press, December 2010. - Wen-tau Yih and Chris Meek, Learning Vector Representations for Similarity Measures, no. MSR-TR-2010-139, 25 October 2010.
- Christopher Meek and Bo Thiesson, Probabilistic Inference for CART network, no. MSR-TR-2010-40, April 2010.
- Asela Gunawardana, Tim Paek, and Christopher Meek, Usability Guided Key-Target Resizing for Soft Keyboards, in
*International Conference on Intelligent User Interfaces*, Association for Computing Machinery, Inc., February 2010. - S. Bercovici, C. Meek, Y. Wexler, and D. Geiger, Estimating Genomewide IBD-sharing from SNP data via an efficient hidden Markov model of LD with application to gene mapping, in
*Bioinformatics*, vol. 26, no. 12, pp. 175-182, Oxford University Press, 2010.