Pedro Domingos
A Few Useful Things to Know about Machine Learning
P. Domingos, Communications of the ACM 55:10, 2012.
A Tractable First-Order Probabilistic Logic
P. Domingos, A. Webb, Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012.
Learning Multiple Hierarchical Relational Clusterings
A. Nath, P. Domingos, ICML-12 Workshop on Statistical Relational Learning, 2012.
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
C. Kiddon, P. Domingos, AAAI Conference on Artificial Intelligence, 2011.
Implementing Weighted Abduction in Markov Logic
J. Blythe, J. Hobbs, P. Domingos, R. Kate, R. Mooney, International Conference on Computational Semantics, 2011.
Approximation by Quantization
V. Gogate, P. Domingos, Uncertainty in Artificial Intelligence, 2011.
Probabilistic Theorem Proving
V. Gogate, P. Domingos, Uncertainty in Artificial Intelligence, 2011.
Sum-Product Networks: A New Deep Architecture
H. Poon, P. Domingos, Uncertainty in Artificial Intelligence, 2011.
Approximate Lifted Belief Propagation
P. Singla, A. Nath, P. Domingos, Workshop on Statistical Relational AI, 2010.
Efficient Belief Propagation for Utility Maximization and Repeated Inference
A. Nath, P. Domingos, AAAI Conference on Artificial Intelligence, 2010.
Exploiting Logical Structure in Lifted Probabilistic Inference
V. Gogate, P. Domingos, Workshop on Statistical Relational AI, 2010.
Formula-Based Probabilistic Inference
V. Gogate, P. Domingos, Uncertainty in Artificial Intelligence, 2010.
Leveraging Ontologies for Lifted Probabilistic Inference and Learning
C. Kiddon, P. Domingos, Workshop on Statistical Relational AI, 2010.
Machine Reading: A "Killer App" for Statistical Relational AI
H. Poon, P. Domingos, Workshop on Statistical Relational AI, 2010.
Unsupervised Ontology Induction from Text
H. Poon, P. Domingos, Annual Meeting of the Association for Computational Linguistics, 2010.
Bottom-Up Learning of Markov Network Structure
J. Davis, P. Domingos, International Conference on Machine Learning, 2010.
Learning Markov Logic Networks Using Structural Motifs
S. Kok, P. Domingos, International Conference on Machine Learning, 2010.
Machine Reading at the University of Washington
H. Poon, J. Christensen, P. Domingos, O. Etzioni, R. Hoffmann, C. Kiddon, Mausam, A. Ritter, S. Soderland, D. Weld, F. Wu, T. Lin, X. Ling, S. Schoenmackers, Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010.
Approximate Inference by Compilation to Arithmetic Circuits
D. Lowd, P. Domingos, Annual Conference on Neural Information Processing Systems, 2010.
Learning Efficient Markov Networks
V. Gogate, W. Webb, P. Domingos, Annual Conference on Neural Information Processing Systems, 2010.
Unsupervised Semantic Parsing
H. Poon, P. Domingos, Conference on Empirical Methods in Natural Language Processing, 2009.
A Language for Relational Decision Theory
A. Nath, P. Domingos, International Workshop on Statistical Relational Learning, 2009.
Deep Transfer via Second-Order Markov Logic
J. Davis, P. Domingos, International Conference on Machine Learning, 2009.
Competitive Learning for Deep Temporal Networks
R. Gens, P. Domingos, Annual Conference on Neural Information Processing Systems, 2009.
Learning Markov Logic Network Structure via Hypergraph Lifting
S. Kok, P. Domingos, International Conference on Machine Learning, 2009.
A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC
H. Poon, P. Domingos, M. Sumner, AAAI Conference on Artificial Intelligence, 2008.
Hybrid Markov Logic Networks
J. Wang, P. Domingos, AAAI Conference on Artificial Intelligence, 2008.
Lifted First-Order Belief Propagation
P. Singla, P. Domingos, AAAI Conference on Artificial Intelligence, 2008.
Joint Unsupervised Coreference Resolution with Markov Logic
H. Poon, P. Domingos, Conference on Empirical Methods in Natural Language Processing, 2008.
Extracting Semantic Networks from Text via Relational Clustering
S. Kok, P. Domingos, European Conference on Machine Learning, 2008.
Learning Arithmetic Circuits
D. Lowd, P. Domingos, Uncertainty in Artificial Intelligence, 2008.
Joint Inference in Information Extraction
H. Poon, P. Domingos, AAAI Spring Symposium Series, 2007.
Recursive Random Fields
D. Lowd, P. Domingos, International Joint Conference on Artificial Intelligence, 2007.
Efficient Weight Learning for Markov Logic Networks
D. Lowd, P. Domingos, European Conference on Principles and Practice of Knowledge Discovery in Databases, 2007.
Markov Logic in Infinite Domains
P. Singla, P. Domingos, Uncertainty in Artificial Intelligence, 2007.
Statistical Predicate Invention
S. Kok, P. Domingos, International Conference on Machine Learning, 2007.
Structured Machine Learning: Ten Problems for the Next Ten Years
P. Domingos, International Conference on Inductive Logic Programming, 2007.
Entity Resolution with Markov Logic
P. Singla, P. Domingos, IEEE International Conference on Data Mining, 2006.
Markov Logic Networks
M. Richardson, P. Domingos, Machine Learning Journal, 2006.
Memory-Efficient Inference in Relational Domains
P. Singla, P. Domingos, AAAI Conference on Artificial Intelligence, 2006.
Sound and Efficient Inference with Probabilistic and Deterministic Dependencies
H. Poon, P. Domingos, AAAI Conference on Artificial Intelligence, 2006.
Unifying Logical and Statistical AI
P. Domingos, S. Kok, H. Poon, M. Richardson, P. Singla, AAAI Conference on Artificial Intelligence, 2006.
Discriminative Training of Markov Logic Networks
P. Singla, P. Domingos, AAAI Conference on Artificial Intelligence, 2005.
Learning the Structure of Markov Logic Networks
S. Kok, P. Domingos, International Conference on Machine Learning, 2005.
Mining social networks for viral marketing
P. Domingos, IEEE Intelligent Systems, 2005.
Object identification with attribute-mediated dependencies
P. Singla, P. Domingos, European Conference on Principles and Practice of Knowledge Discovery in Databases, 2005.
Naive Bayes Models for Probability Estimation
D. Lowd, P. Domingos, International Conference on Machine Learning, 2005.
iMAP: Discovering Complex Semantic Matches between Database Schemas
R. Dhamankar, Y. Lee, A.H. Doan, A. Halevy, P. Domingos, ACM SIGMOD International Conference on Management of Data, 2004.
Adversarial Classification
N. Dalvi, P. Domingos, Mausam, S. Sanghai, D. Verma, Knowledge Discovery and Data Mining, 2004.
Multi-relational record linkage
P. Singla, P. Domingos, KDD Workshop on Multi-Relational Data Mining, 2004.
Trust management for the Semantic Web
M. Richardson, R. Agrawal, P. Domingos, International Semantic Web Conference, 2003.
Building large knowledge bases by mass collaboration
M. Richardson, P. Domingos, International Conference on Knowledge Capture, 2003.
Learning with knowledge from multiple experts
M. Richardson, P. Domingos, International Conference on Machine Learning, 2003.
Mining massive relational databases
G. Hulten, P. Domingos, Y. Abe, International Workshop on Statistical Relational Learning, 2003.
Research on Statistical Relational Learning at the University of Washington
P. Domingos, Y. Abe, C. Anderson, A. Doan, D. Fox, A. Halevy, G. Hulten, H. Kautz, T. Lau, L. Liao, J. Madhavan, D. Mausam, M. Richardson, S. Sanghai, D. Weld, S. Wolfman, Proc. of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data, 2003.
Mining knowledge-sharing sites for viral marketing
M. Richardson, P. Domingos, Knowledge Discovery and Data Mining, 2002.
Learning from Infinite Data in Finite Time
P. Domingos, G. Hulten, Annual Conference on Neural Information Processing Systems, 2002.
Learning to Map between Ontologies on the Semantic Web
A.H. Doan, J. Madhavan, P. Domingos, A. Halevy, International World Wide Web Conference, 2002.
Mining Complex Models from Arbitrarily Large Databases in Constant Time
G. Hulten, P. Domingos, Knowledge Discovery and Data Mining, 2002.
Relational Markov models and their application to adaptive Web navigation
C. Anderson, P. Domingos, D. Weld, Knowledge Discovery and Data Mining, 2002.
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
P. Domingos, G. Hulten, International Conference on Machine Learning, 2001.
Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach
A.H. Doan, P. Domingos, A. Halevy, ACM SIGMOD International Conference on Management of Data, 2001.
Mining the network value of customers
P. Domingos, M. Richardson, Knowledge Discovery and Data Mining, 2001.
Mining Time-Changing Data Streams
G. Hulten, P. Domingos, L. Spencer, Knowledge Discovery and Data Mining, 2001.
The intelligent surfer: Probabilistic combination of link and content information in PageRank
M. Richardson, P. Domingos, Annual Conference on Neural Information Processing Systems, 2001.
Mining high-speed data streams
G. Hulten, P. Domingos, Knowledge Discovery and Data Mining, 2000.
Professor
Office: CSE 648
Email: pedrod
cs
Phone: (206) 543-4229
Fax: (206) 543-2969
Mail:
Dept. of Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA 98195-2350
