Jesse Davis

Professor

Address:
Dept. of Computer Science
Katholieke Universiteit Leuven
Celestijnenlaan 200A
3001 Heverlee
Belgium

Telephone:
Fax: +32 16 32 79 96
Email: first name + dot + last name at cs dot kuleuven dot be
Office: 1.06

About Me

I joined K.U. Leuven in October 2010. I am a member of the Machine Learning group within the Declarative Languages and Artificial Intelligence Lab (DTAI).

I did a post-doc with Pedro Domingos at the University of Washington. I worked on Markov logic networks, which are publicly available as the Alchemy system. I am also involved with our recent MURI award entitled A Unified Approach to Abductive Inference. I did my Ph.D at the University of Wisconsin-Madison under the supervision of David Page.

News

We are organizing a workshop on sports analytics at ECML/PKDD 2013.

Education

Ph.D in Computer Sciences from the University of Wisconsin-Madison. (August 2007)

M.S. in Computer Sciences from the University of Wisconsin-Madison. (May 2005)

B.A. in Computer Science from Williams College. (June 2002)

Current PhD Students

Daniele Alfarone

Marc Mertens (joint with Bart Vanrumste)

Irma Ravkic

Jan Van Haaren

Post-docs

Nima Taghipour (Joint with Hendrik Blockeel)

Martin Znidarsic

Former PhD Students

Nima Taghipour, graduated March 2013 (Primary advisor: Hendrik Blockeel)

Software

I have written a c++ package for learning the structure of a Markov network. It can be found here.

Jan Van Haaren and I have another system, called GSSL, for learning the structure of a Markov network.

Mark Goadrich and I have written a java package for generating ROC and Precision Recall curves. It can also calculate the area under the curve. The jar file is available here. For more information about ROC and PR curves, see our paper at ICML 2006.

Publications

Journals

Nima Taghipour, Daan Fierens, Jesse Davis and Hendrik Blockeel (2013). Lifted Variable Elimination: Decoupling the Operators from the Constraint Language. Accepted for publication in the Journal of Artificial Intelligence Research.

Jesse Davis and Pedro Domingos (2011). Deep Transfer: A Markov Logic Approach. AI Magazine.

Elizabeth S. Burnside, Jesse Davis, Jagpreet Chhatwal, Oguzhan Alagoz, Mary J. Lindstrom, Berta M. Geller,Benjamin Littenberg, Katherine A. Shaffer, Charles E. Kahn, Jr, and C. David Page (2009). Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings. In Radiology. Available here.

Refereed Conferences

Nima Taghipour, Guy Van Den Broeck, Daan Fierens, Jesse Davis and Hendrik Blockeel (2013). Completeness Results for Lifted Variable Elimination. To appear in the Proceedings of Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2013) . Will be available shortly.

Bogdan Moldovan, Ingo Thon, Jesse Davis and Luc De Raedt (2013). MCMC Estimation of Conditional Probabilities in Probabilistic Programming Languages. To appear in the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2013). Will be available shortly.

Daan Fierens, Kristian Kersting, Jesse Davis, Jian Chen, and Martin Mladenov, (2013). Pairwise Markov logic. To appear in the Post Proceedings of the 22nd International Conference on Inductive Logic Programming (ILP 2012) Will be available shortly.

Jesse Davis, Bess Berg, David Page, Vitor Santos Costa, Peggy Peissig and Michael Caldwell (2012). Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events. In the Proceedings of the Twenty-Ninth International Conference on Machine Learning (ICML 2012). Available from lirias.

Kendrick Boyd, David Page, Vitor Santos Costa and Jesse Davis (2012). Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation. In the Proceedings of the Twenty-Ninth International Conference on Machine Learning (ICML 2012). Available as PDF

Jan Van Haaren and Jesse Davis (2012). Markov Network Structure Learning: A Randomized Feature Generation Approach. In the Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012). Available from lirias

Guy Van Den Broeck and Jesse Davis (2012). Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference. In the Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012). Available from lirias.

Nima Taghipour, Daan Fierens, Jesse Davis and Hendrik Blockeel (2012). Lifted Variable Elimination with Arbitrary Constraints. In the Proceedings of Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012) . Available from lirias.

Guy Van Den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis and Luc De Raedt (2011). Lifted Probabilistic Inference by First-Order Knowledge Compilation. Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI). Available from lirias.

Daniel Lowd and Jesse Davis (2010). Learning Markov Network Structure with Decision Trees. Proceedings of IEEE International Conference on Data Mining (ICDM 2010). Available as PDF.

Stefan Schoenmackers, Jesse Davis, Oren Etzioni, and Daniel S. Weld (2010). Learning First-Order Horn Clauses from Web Text. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP 2010). Available as PDF

Jesse Davis and Pedro Domingos (2010). Bottom-Up Learning of Markov Network Structure. In the Proceedings of the 27th International Conference on Machine Learning (ICML). Available as PDF.

Jesse Davis and Pedro Domingos (2009). Deep Transfer via Second-Order Markov Logic. In the Proceedings of the 26th International Conference on Machine Learning (ICML). Available as PDF.

Jesse Davis, Vitor Santos Costa, Soumya Ray and David Page (2007). An Integrated Approach to Feature Construction and Model Building for Drug Activity Prediction. In the Proceedings of the 24th International Conference on Machine Learning (ICML). Available as PDF.

Jesse Davis, Irene Ong, Jan Struyf, Elizabeth Burnside, David Page and Vitor Santos Costa (2007). Change of Representation for Statistical Relational Learning. In the Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI). Available as PDF.

Jesse Davis and Mark Goadrich (2006). The Relationship Between Precision-Recall and ROC Curves. In the Proceedings of the 23rd International Conference on Machine Learning (ICML). Available as PDF.

Jan Struyf, Jesse Davis and David Page (2006). An Efficient Approximation to Lookahead in Relational Learners. In the Proceedings of the 17th European Conference on Machine Learning (ECML). Available as PDF.

Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Raghu Ramakrishnan, Vitor Santos Costa and Jude Shavlik (2005). View Learning for Statistical Relational Learning: With an Application to Mammography. In the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). Available as postscript or PDF.

Jesse Davis, Ines Dutra, David Page and Vitor Santos Costa (2005). Establishing Identity Equivalence in Multi-Relational Domains. In the Proceedings of the International Conference on Intelligence Analysis. Available as postscript, PDF or Word. Winner of the Best Technical Paper award.

Elizabeth S. Burnside, Jesse Davis, Vitor Santos Costa, Ines Dutra, Charles E. Kahn Jr., Jason P. Fine and David Page (2005). Knowledge Discovery from Structured Mammography Reports Using Inductive Logic Programming. In the Proceeding of the American Medical Informatics Association Annual Symposium (AMIA). Available as PDF.

Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Vitor Santos Costa (2005). An Integrated Approach to Learning Bayesian Networks of Rules. In the Proceedings of the European Conference of Machine Learning (ECML). Available as postscript or PDF.

Book Chapters

Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Raghu Ramakrishnan, Vitor Santos Costa and Jude Shavlik (2007). Learning a New View of a Database: With an Application to Mammography. In L. Getoor and B. Taskar, editors, An Introduction to Statistical Relational Learning, MIT Press.

Refereed Workshops

Mathias Verbeke and Jesse Davis (2012). A text mining approach as baseline for QA4MRE'12. Conference and Labs of the Evaluation Forum (CLEF).

Jesse Davis and Pedro Domingos (2008). Deep Transfer via Second-Order Markov Logic. In the Proceedings of the AAAI-2008 Workshop on Transfer Learning for Complex Tasks. Chicago, IL: AAAI Press. Available as PDF

Jesse Davis, Eric Lantz, David Page, Jan Struyf, Peggy Peissig, Humberto Vidaillet and Michael Caldwell (2008). Machine Learning for Personalized Medicine: Will this Drug Give Me a Heart Attack? In the Proceedings of Machine Learning in Health Care Applications Workshop. In conjunction with ICML 2008.

Jesse Davis, Elizabeth Burnside, David Page and Vitor Santos Costa (2006). View Learning Extended: Inventing New Tables for Statistical Relational Learning. In the Proceedings of Open Problems in Statistical Relational Learning Workshop (SRL2006). In conjunction with ICML 2006.

Jesse Davis, Vitor Santos Costa, Irene Ong, David Page and Ines Dutra (2004). Using Bayesian Classifiers to Combine Rules. In the Proceedings of the 3rd Workshop on Multi-Relational Data Mining ( MRDM 2004). In conjunction with KDD 2004. Available as postscript or PDF.

Teaching

I teach a class on Data Mining as a part of the master in artificial intelligence program.

Seminars

When I was at the Univeristy of Washington, I ran the machine learning reading group.

In the spring of 2008, I ran a seminar on Biomedical Applications of Artificial Intelligence.

Professional Service

Area Chair for ECML/PKDD 2013
Program Committe for the 30th International Conference on Machine Learning (ICML-13)
Program Committee for the 27th Conference on Artificial Intelligence (AAAI-13)
Program Committe for ECML/PKDD 2012
Program Committe for the 29th International Conference on Machine Learning (ICML-12)
Program Committee for the 26th Conference on Artificial Intelligence (AAAI-12)
Program Committee for the European Conference on Artificial Intelligence (ECAI-12)
Program Committe for the 28th International Conference on Machine Learning (ICML-11)
Program Committee for the 25th Conference on Artificial Intelligence (AAAI-11)
Program Committee for the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11)
Program Committe for the 27th International Conference on Machine Learning (ICML-10)
Program Committee for the 24th Conference on Artificial Intelligence (AAAI-10)
Program Committee for the 2010 IEEE International Conference on Data Mining (ICDM-10)
Program Committee for the 13th International Conference on Discovery Science (DS-10)
Program Committee for the 21st International Joint Conference on Artificial Intelligence (IJCAI-09)
Program Committee for the 12th International Conference on Discovery Science (DS-09)
Program Committee for the 23rd Conference on Artificial Intelligence (AAAI-08)
Program Committee for the 25th International Conference on Machine Learning (ICML-08)
Program Committee for the 2008 IEEE International Conference on Data Mining (ICDM-08)
Program Committee for the 19th Brazilian Symposium on Artificial Intelligence (SBIA-08)
Reviewer for the Artificial Intelligence Journal (2010)
Reviewer for the Machine Learning Journal (2007,2008,2009,2010)
Reviewer for Transactions on Pattern Analysis and Machine Intelligence (2009)
Reviewer for the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) (2009)
Reviewer for the SRL Workshop (2009)
Reviewer for the ACM Transactions on Knowledge Discovery from Data (2007,2008)
Reviewer for the Journal of Artificial Intelligence Research (2007,2008)
Reviewer for the Annals of Mathematics and Artificial Intelligence (2008)
Reviewer for the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB-07)
Reviewer for the 20th International Joint Conference on Artificial Intelligence (IJCAI-07)
Reviewer for the ACM Transactions on Computational Logic (2006)