Pedro Domingos

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2011
114	Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models. Chloe Kiddon, Pedro Domingos. AAAI 2011. Web SearchBibTeXDownload
113	Deep Transfer: A Markov Logic Approach. Jesse Davis, Pedro Domingos. AI Magazine (32): 51-53 (2011). Web SearchBibTeXDownload
112	Relational Dynamic Bayesian Networks. Pedro Domingos, Sumit K. Sanghai, Daniel S. Weld. CoRR (abs/1109.2137) (2011). Web SearchBibTeXDownload
111	Sum-product networks: A new deep architecture. Hoifung Poon, Pedro Domingos. ICCV Workshops 2011, 689-690. Web SearchBibTeXDownload
110	Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning. Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro Domingos, Kristian Kersting, Xifeng Yan. Machine Learning (83): 133-135 (2011). Web SearchBibTeXDownload
109	Approximation by Quantization. Vibhav Gogate, Pedro Domingos. UAI 2011, 247-255. Web SearchBibTeXDownload
108	Probabilistic Theorem Proving. Vibhav Gogate, Pedro Domingos. UAI 2011, 256-265. Web SearchBibTeXDownload
107	Sum-Product Networks: A New Deep Architecture. Hoifung Poon, Pedro Domingos. UAI 2011, 337-346. Web SearchBibTeXDownload
2010
106	Efficient Lifting for Online Probabilistic Inference. Aniruddh Nath, Pedro Domingos. AAAI 2010. Web SearchBibTeXDownload
105	Efficient Belief Propagation for Utility Maximization and Repeated Inference. Aniruddh Nath, Pedro Domingos. AAAI 2010. Web SearchBibTeXDownload
104	Unsupervised Ontology Induction from Text. Hoifung Poon, Pedro Domingos. ACL 2010, 296-305. Web SearchBibTeXDownload
103	Bottom-Up Learning of Markov Network Structure. Jesse Davis, Pedro Domingos. ICML 2010, 271-278. Web SearchBibTeXDownload
102	Learning Markov Logic Networks Using Structural Motifs. Stanley Kok, Pedro Domingos. ICML 2010, 551-558. Web SearchBibTeXDownload
101	Learning Efficient Markov Networks. Vibhav Gogate, William Austin Webb, Pedro Domingos. NIPS 2010, 748-756. Web SearchBibTeXDownload
100	Approximate Inference by Compilation to Arithmetic Circuits. Daniel Lowd, Pedro Domingos. NIPS 2010, 1477-1485. Web SearchBibTeXDownload
99	Formula-Based Probabilistic Inference. Vibhav Gogate, Pedro Domingos. UAI 2010, 210-219. Web SearchBibTeXDownload
2009
98	Unsupervised Semantic Parsing. Hoifung Poon, Pedro Domingos. EMNLP 2009, 1-10. Web SearchBibTeXDownload
97	Deep transfer via second-order Markov logic. Jesse Davis, Pedro Domingos. ICML 2009, 28. Web SearchBibTeXDownload
96	Learning Markov logic network structure via hypergraph lifting. Stanley Kok, Pedro Domingos. ICML 2009, 64. Web SearchBibTeXDownload
95	Markov Logic: An Interface Layer for Artificial Intelligence. Pedro Domingos, Daniel Lowd. Markov Logic: An Interface Layer for Artificial Intelligence 2009. Web SearchBibTeXDownload
2008
94	Lifted First-Order Belief Propagation. Parag Singla, Pedro Domingos. AAAI 2008, 1094-1099. Web SearchBibTeX
93	A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC. Hoifung Poon, Pedro Domingos, Marc Sumner. AAAI 2008, 1075-1080. Web SearchBibTeX
92	Hybrid Markov Logic Networks. Jue Wang, Pedro Domingos. AAAI 2008, 1106-1111. Web SearchBibTeX
91	Markov logic: a unifying language for knowledge and information management. Pedro Domingos. CIKM 2008, 519. Web SearchBibTeXDownload
90	Extracting Semantic Networks from Text Via Relational Clustering. Stanley Kok, Pedro Domingos. ECML/PKDD (1) 2008, 624-639. Web SearchBibTeXDownload
89	Joint Unsupervised Coreference Resolution with Markov Logic. Hoifung Poon, Pedro Domingos. EMNLP 2008, 650-659. Web SearchBibTeXDownload
88	Structured machine learning: the next ten years. Thomas G. Dietterich, Pedro Domingos, Lise Getoor, Stephen Muggleton, Prasad Tadepalli. Machine Learning (73): 3-23 (2008). Cited by 8Web SearchBibTeXDownload
87	Markov Logic. Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla. Probabilistic Inductive Logic Programming 2008, 92-117. Web SearchBibTeXDownload
86	Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition. Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla, Marc Sumner, Jue Wang. SSPR/SPR 2008, 3. Web SearchBibTeXDownload
85	Learning Arithmetic Circuits. Daniel Lowd, Pedro Domingos. UAI 2008, 383-392. Web SearchBibTeXDownload
84	Just Add Weights: Markov Logic for the Semantic Web. Pedro Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla. URSW (LNCS Vol.) 2008, 1-25. Web SearchBibTeXDownload
2007
83	Joint Inference in Information Extraction. Hoifung Poon, Pedro Domingos. AAAI 2007, 913-918. Web SearchBibTeX
82	Toward knowledge-rich data mining. Pedro Domingos. Data Min. Knowl. Discov. (15): 21-28 (2007). Web SearchBibTeXDownload
81	Statistical predicate invention. Stanley Kok, Pedro Domingos. ICML 2007, 433-440. Web SearchBibTeXDownload
80	Recursive Random Fields. Daniel Lowd, Pedro Domingos. IJCAI 2007, 950-955. Web SearchBibTeXDownload
79	Efficient Weight Learning for Markov Logic Networks. Daniel Lowd, Pedro Domingos. PKDD 2007, 200-211. Web SearchBibTeXDownload
78	Markov Logic in Infinite Domains. Pedro Domingos, Parag Singla. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007. Web SearchBibTeXDownload
2006
77	Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. Hoifung Poon, Pedro Domingos. AAAI 2006. Web SearchBibTeX
76	Memory-Efficient Inference in Relational Domains. Parag Singla, Pedro Domingos. AAAI 2006. Web SearchBibTeX
75	Unifying Logical and Statistical AI. Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla. AAAI 2006. Web SearchBibTeX
74	Learning, Logic, and Probability: A Unified View. Pedro Domingos. IBERAMIA-SBIA 2006, 3. Web SearchBibTeXDownload
73	Entity Resolution with Markov Logic. Parag Singla, Pedro Domingos. ICDM 2006, 572-582. Web SearchBibTeXDownload
72	Markov logic networks. Matthew Richardson, Pedro Domingos. Machine Learning (62): 107-136 (2006). Web SearchBibTeXDownload
2005
71	Discriminative Training of Markov Logic Networks. Parag Singla, Pedro Domingos. AAAI 2005, 868-873. Web SearchBibTeX
70	Reports on the 2005 AAAI Spring Symposium Series. Michael L. Anderson, Thomas Barkowsky, Pauline Berry, Douglas S. Blank, Timothy Chklovski, Pedro Domingos, Marek J. Druzdzel, Christian Freksa, John Gersh, Mary Hegarty, Tze-Yun Leong, Henry Lieberman, Ric K. Lowe, Susann Luperfoy, Rada Mihalcea, Lisa Meeden, David P. Miller, Tim Oates, Robert Popp, Daniel Shapiro, Nathan Schurr, Push Singh, John Yen. AI Magazine (26): 87-92 (2005). Web SearchBibTeXDownload
69	An Efficient and Scalable Architecture for Neural Networks with Backpropagation Learning. Pedro Domingos, Fernando M. Silva, Horácio C. Neto. FPL 2005, 89-94. Web SearchBibTeX
68	Naive Bayes models for probability estimation. Daniel Lowd, Pedro Domingos. ICML 2005, 529-536. Web SearchBibTeXDownload
67	Learning the structure of Markov logic networks. Stanley Kok, Pedro Domingos. ICML 2005, 441-448. Web SearchBibTeXDownload
66	Social Networks Applied. Steffen Staab, Pedro Domingos, Peter Mika, Jennifer Golbeck, Li Ding, Timothy W. Finin, Anupam Joshi, Andrzej Nowak, Robin R. Vallacher. IEEE Intelligent Systems (20): 80-93 (2005). Cited by 100Web SearchBibTeXDownload
65	Collective Object Identification. Parag Singla, Pedro Domingos. IJCAI 2005, 1636-1637. Web SearchBibTeXDownload
64	Object Identification with Attribute-Mediated Dependences. Parag Singla, Pedro Domingos. PKDD 2005, 297-308. Web SearchBibTeXDownload
2004
63	Ontology Matching: A Machine Learning Approach. AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Y. Halevy. Handbook on Ontologies 2004, 385-404. Cited by 211Web SearchBibTeX
62	Learning Bayesian network classifiers by maximizing conditional likelihood. Daniel Grossman, Pedro Domingos. ICML 2004. Web SearchBibTeXDownload
61	Learning, Logic, and Probability: A Unified View. Pedro Domingos. ILP 2004, 359. Web SearchBibTeXDownload
60	Adversarial classification. Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. Sanghai, Deepak Verma. KDD 2004, 99-108. Web SearchBibTeXDownload
59	Real-World Learning with Markov Logic Networks. Pedro Domingos. PKDD 2004, 17. Web SearchBibTeXDownload
58	iMAP: Discovering Complex Mappings between Database Schemas. Robin Dhamankar, Yoonkyong Lee, AnHai Doan, Alon Y. Halevy, Pedro Domingos. SIGMOD Conference 2004, 383-394. Cited by 62Web SearchBibTeXDownload
57	Combining Link and Content Information in Web Search. Matthew Richardson, Pedro Domingos. Web Dynamics 2004, 179-194. Web SearchBibTeX
2003
56	Learning from Networks of Examples. Pedro Domingos, Matthew Richardson. EPIA 2003, 5. Web SearchBibTeXDownload
55	Learning with Knowledge from Multiple Experts. Matthew Richardson, Pedro Domingos. ICML 2003, 624-631. Web SearchBibTeX
54	Automatically Personalizing User Interfaces. Daniel S. Weld, Corin R. Anderson, Pedro Domingos, Oren Etzioni, Krzysztof Gajos, Tessa A. Lau, Steven A. Wolfman. IJCAI 2003, 1613-1619. Web SearchBibTeX
53	Trust Management for the Semantic Web. Matthew Richardson, Rakesh Agrawal, Pedro Domingos. International Semantic Web Conference 2003, 351-368. Cited by 275Web SearchBibTeXDownload
52	Learning programs from traces using version space algebra. Tessa A. Lau, Pedro Domingos, Daniel S. Weld. K-CAP 2003, 36-43. Web SearchBibTeXDownload
51	Building large knowledge bases by mass collaboration. Matthew Richardson, Pedro Domingos. K-CAP 2003, 129-137. Web SearchBibTeXDownload
50	Learning to Match the Schemas of Data Sources: A Multistrategy Approach. AnHai Doan, Pedro Domingos, Alon Y. Halevy. Machine Learning (50): 279-301 (2003). Cited by 158Web SearchBibTeXDownload
49	Programming by Demonstration Using Version Space Algebra. Tessa A. Lau, Steven A. Wolfman, Pedro Domingos, Daniel S. Weld. Machine Learning (53): 111-156 (2003). Web SearchBibTeXDownload
48	Tree Induction for Probability-Based Ranking. Foster J. Provost, Pedro Domingos. Machine Learning (52): 199-215 (2003). Web SearchBibTeXDownload
47	Prospects and challenges for multi-relational data mining. Pedro Domingos. SIGKDD Explorations (5): 80-83 (2003). Web SearchBibTeXDownload
46	Learning to match ontologies on the Semantic Web. AnHai Doan, Jayant Madhavan, Robin Dhamankar, Pedro Domingos, Alon Y. Halevy. VLDB J. (12): 303-319 (2003). Cited by 250Web SearchBibTeXDownload
2002
45	Representing and Reasoning about Mappings between Domain Models. Jayant Madhavan, Philip A. Bernstein, Pedro Domingos, Alon Y. Halevy. AAAI/IAAI 2002, 80-86. Cited by 207Web SearchBibTeX
44	Mining complex models from arbitrarily large databases in constant time. Geoff Hulten, Pedro Domingos. KDD 2002, 525-531. Web SearchBibTeXDownload
43	Mining knowledge-sharing sites for viral marketing. Matthew Richardson, Pedro Domingos. KDD 2002, 61-70. Web SearchBibTeXDownload
42	Relational Markov models and their application to adaptive web navigation. Corin R. Anderson, Pedro Domingos, Daniel S. Weld. KDD 2002, 143-152. Web SearchBibTeXDownload
41	When and How to Subsample: Report on the KDD-2001 Panel. Pedro Domingos. SIGKDD Explorations (3): 74-75 (2002). Web SearchBibTeXDownload
40	Learning to map between ontologies on the semantic web. AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Y. Halevy. WWW 2002, 662-673. Cited by 777Web SearchBibTeXDownload
2001
39	Catching up with the Data: Research Issues in Mining Data Streams. Pedro Domingos, Geoff Hulten. DMKD 2001. Web SearchBibTeXDownload
38	A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering. Pedro Domingos, Geoff Hulten. ICML 2001, 106-113. Web SearchBibTeX
37	Adaptive Web Navigation for Wireless Devices. Corin R. Anderson, Pedro Domingos, Daniel S. Weld. IJCAI 2001, 879-884. Web SearchBibTeX
36	Mixed initiative interfaces for learning tasks: SMARTedit talks back. Steven A. Wolfman, Tessa A. Lau, Pedro Domingos, Daniel S. Weld. IUI 2001, 167-174. Web SearchBibTeXDownload
35	Mining time-changing data streams. Geoff Hulten, Laurie Spencer, Pedro Domingos. KDD 2001, 97-106. Web SearchBibTeXDownload
34	Mining the network value of customers. Pedro Domingos, Matthew Richardson. KDD 2001, 57-66. Web SearchBibTeXDownload
33	The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank. Matthew Richardson, Pedro Domingos. NIPS 2001, 1441-1448. Web SearchBibTeXDownload
32	Learning from Infinite Data in Finite Time. Pedro Domingos, Geoff Hulten. NIPS 2001, 673-680. Web SearchBibTeXDownload
31	Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach. AnHai Doan, Pedro Domingos, Alon Y. Halevy. SIGMOD Conference 2001, 509-520. Cited by 617Web SearchBibTeXDownload
30	Personalizing Web Sites for Mobile Users. Corin R. Anderson, Pedro Domingos, Daniel S. Weld. WWW 2001, 565-575. Web SearchBibTeXDownload
2000
29	A Unified Bias-Variance Decomposition for Zero-One and Squared Loss. Pedro Domingos. AAAI/IAAI 2000, 564-569. Web SearchBibTeX
28	Beyond Occam's Razor: Process-Oriented Evaluation. Pedro Domingos. ECML 2000, 3. Web SearchBibTeXDownload
27	A Unifeid Bias-Variance Decomposition and its Applications. Pedro Domingos. ICML 2000, 231-238. Web SearchBibTeX
26	Version Space Algebra and its Application to Programming by Demonstration. Tessa A. Lau, Pedro Domingos, Daniel S. Weld. ICML 2000, 527-534. Web SearchBibTeX
25	Bayesian Averaging of Classifiers and the Overfitting Problem. Pedro Domingos. ICML 2000, 223-230. Web SearchBibTeX
24	Mining high-speed data streams. Pedro Domingos, Geoff Hulten. KDD 2000, 71-80. Web SearchBibTeXDownload
23	Learning Source Description for Data Integration. AnHai Doan, Pedro Domingos, Alon Y. Levy. WebDB (Informal Proceedings) 2000, 81-86. Cited by 140Web SearchBibTeXDownload
1999
22	The Role of Occam's Razor in Knowledge Discovery. Pedro Domingos. Data Min. Knowl. Discov. (3): 409-425 (1999). Web SearchBibTeXDownload
21	Process-Oriented Estimation of Generalization Error. Pedro Domingos. IJCAI 1999, 714-721. Web SearchBibTeX
20	MetaCost: A General Method for Making Classifiers Cost-Sensitive. Pedro Domingos. KDD 1999, 155-164. Web SearchBibTeXDownload
1998
19	A Process-Oriented Heuristic for Model Selection. Pedro Domingos. ICML 1998, 127-135. Web SearchBibTeX
18	Knowledge Discovery Via Multiple Models. Pedro Domingos. Intell. Data Anal. (2): 187-202 (1998). Web SearchBibTeXDownload
17	Occam's Two Razors: The Sharp and the Blunt. Pedro Domingos. KDD 1998, 37-43. Web SearchBibTeX
1997
16	A Comparison of Model Averaging Methods in Foreign Exchange Prediction. Pedro Domingos. AAAI/IAAI 1997, 828. Web SearchBibTeX
15	Learning Multiple Models without Sacrificing Comprehensibility. Pedro Domingos. AAAI/IAAI 1997, 829. Web SearchBibTeX
14	Control-Sensitive Feature Selection for Lazy Learners. Pedro Domingos. Artif. Intell. Rev. (11): 227-253 (1997). Web SearchBibTeXDownload
13	Knowledge Acquisition form Examples Vis Multiple Models. Pedro Domingos. ICML 1997, 98-106. Web SearchBibTeX
12	Why Does Bagging Work? A Bayesian Account and its Implications. Pedro Domingos. KDD 1997, 155-158. Web SearchBibTeX
11	On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Pedro Domingos, Michael J. Pazzani. Machine Learning (29): 103-130 (1997). Web SearchBibTeXDownload
1996
10	Simple Bayesian Classifiers Do Not Assume Independence. Pedro Domingos, Michael J. Pazzani. AAAI/IAAI, Vol. 2 1996, 1386. Web SearchBibTeX
9	Fast Discovery of Simple Rules. Pedro Domingos. AAAI/IAAI, Vol. 2 1996, 1384. Web SearchBibTeX
8	Multistrategy Learning: A Case Study. Pedro Domingos. AAAI/IAAI, Vol. 2 1996, 1385. Web SearchBibTeX
7	Towards a Unified Approach to Concept Learning. Pedro Domingos. AAAI/IAAI, Vol. 2 1996, 1361. Web SearchBibTeX
6	Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. Pedro Domingos, Michael J. Pazzani. ICML 1996, 105-112. Web SearchBibTeX
5	Efficient Specific-to-General Rule Induction. Pedro Domingos. KDD 1996, 319-322. Web SearchBibTeX
4	Linear-Time Rule Induction. Pedro Domingos. KDD 1996, 96-101. Web SearchBibTeX
3	Unifying Instance-Based and Rule-Based Induction. Pedro Domingos. Machine Learning (24): 141-168 (1996). Web SearchBibTeXDownload
1995
2	Rule Induction and Instance-Based Learning: A Unified Approach. Pedro Domingos. IJCAI 1995, 1226-1232. Web SearchBibTeX
1994
1	The RISE System: Conquering without Separating. Pedro Domingos. ICTAI 1994, 704-707. Web SearchBibTeX

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http://www.cs.washington.edu/homes/pedrod/
University of Washington
USA

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1.	^ AAAI-08 – IAAI-08 Organizing and Program Committees - Retrieved 2011-03-23 - details Original context from http://www.aaai.org/Conferences/AAAI/2008/aaai08committee.php ...y (University of Nebraska-Lincoln) Mark Craven (University of Wisconsin, Madison) Tom Dean (Google) Pedro Domingos (University of Washington) Lise Getoor (University of Maryland) Amy Greenwald (Brown University) Ru...
2.	^ icml2008@helsinki.fi - Retrieved 2011-06-04 - details Original context from http://icml2008.cs.helsinki.fi/organisation.shtml ... Pedro Domingos University of Washington ...
3.	^ KDD 2008: Research Track Program Committee - Retrieved 2009-11-21 - details Original context from http://www.kdd2008.com/rpc.html ...ogram Committee Roberto Bayardo (Google) Soumen Chakrabarti (Indian Institute of Technology Bombay) Pedro Domingos (University of Washington) Charles Elkan (University of California, San Diego) Martin Ester (Simon ...
4.	^ AAAI-07: Program and Schedule - Retrieved 2011-04-08 - details Original context from http://www.aaai.org/Conferences/AAAI/2007/aaai07program.php ...wing, Noisy Sensing and Acting Alfredo Gabaldon, Gerhard Lakemeyer Transfer Learning Session Chair: Pedro Domingos Georgia A Measuring the Level of Transfer Learning by an AP Physics Problem-Solver Matthew Klenk, K...
5.	^ UT-Austin Data Mining Seminar Schedule Abstracts - Retrieved 2010-09-28 - details Original context from http://www.cs.utexas.edu/users/dmg/seminar/Abstracts.html ...eloma Genetics and the Myeloma Institute at the University of Arkansas for Medical Sciences. Pedro Domingos Department of Computer Science and Engineering University of Washington Fri, Oct 25, 2002 Learning ...
6.	^ CIKM 2008 \| CIKM Schedule - Retrieved 2010-11-25 - details Original context from http://www.cikm2008.org/schedule.php ... 8:30-9:30 Keynote : Markov Logic: A Unifying Language for Information and Knowledge Management, Pedro Domingos , (University of Washington, USA) ...
7.	^ KDD 2007 Conference - Tutorials Information - Retrieved 2009-11-21 - details Original context from http://www.sigkdd.org/kdd2007/tutorials.html ...of open research problems in this dynamic area. Statistical Modeling of Relational Data INSTRUCTOR: Pedro Domingos , University of Washington SUMMARY: KDD has traditionally been concerned with mining data from a sin...
8.	^ KDD 2007 Conference - Tutorials Information - Retrieved 2009-11-22 - details Original context from http://www.sigkdd.org/kdd2007/tutorials.html ...cal Framework for Mining Data Streams Please click here for tutorial notes Room 2 Morning Tutorial: Pedro Domingos Statistical Modeling of Relational Data Please click here for tutorial notes Afternoon Tutorial: Du...

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