CSE 648
206-543-2969
pedrodcs.washington.edu
Areas of interest: 

Machine learning, artificial intelligence, data science

Learning Relational Sum-Product Networks

A. Nath, P. DomingosAAAI Conference on Artificial Intelligence , 2015.

Automated Debugging with Tractable Probabilistic Programming

A. Nath, P. DomingosWorkshop on Statistical Relational AI , 2014.

Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond

M. Niepert, P. DomingosWorkshop on Statistical Relational AI , 2014.

Approximate Lifting Techniques for Belief Propagation

P. Singla, A. Nath, P. DomingosAAAI Conference on Artificial Intelligence , 2014.

Learning the Structure of Sum-Product Networks

R. Gens, P. DomingosInternational Conference on Machine LearningOmnipress , 2013.

Tractable Probabilistic Knowledge Bases with Existence Uncertainty

A. Webb, P. DomingosWorkshop on Statistical Relational AI , 2013.

Discriminative Learning of Sum-Product Networks

R. Gens, P. DomingosNeural Information Processing Systems , 2012.

Learning Multiple Hierarchical Relational Clusterings

A. Nath, P. DomingosICML-12 Workshop on Statistical Relational Learning , 2012.

A Few Useful Things to Know about Machine Learning

P. DomingosCommunications of the ACM  55 :10 , 2012.

A Tractable First-Order Probabilistic Logic

P. Domingos, A. WebbTwenty-Sixth AAAI Conference on Artificial Intelligence , 2012.

Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models

C. Kiddon, P. DomingosAAAI Conference on Artificial Intelligence , 2011.

Implementing Weighted Abduction in Markov Logic

J. Blythe, J. Hobbs, P. Domingos, R. Kate, R. MooneyInternational Conference on Computational Semantics , 2011.

Sum-Product Networks: A New Deep Architecture

H. Poon, P. DomingosUncertainty in Artificial Intelligence , 2011.

Probabilistic Theorem Proving

V. Gogate, P. DomingosUncertainty in Artificial Intelligence , 2011.

Approximation by Quantization

V. Gogate, P. DomingosUncertainty in Artificial Intelligence , 2011.

Approximate Inference by Compilation to Arithmetic Circuits

D. Lowd, P. DomingosAnnual Conference on Neural Information Processing Systems , 2010.

Learning Efficient Markov Networks

V. Gogate, W. Webb, P. DomingosAnnual Conference on Neural Information Processing Systems , 2010.

Machine Reading: A "Killer App" for Statistical Relational AI

H. Poon, P. DomingosWorkshop on Statistical Relational AI , 2010.

Leveraging Ontologies for Lifted Probabilistic Inference and Learning

C. Kiddon, P. DomingosWorkshop on Statistical Relational AI , 2010.

Unsupervised Ontology Induction from Text

H. Poon, P. DomingosAnnual Meeting of the Association for Computational Linguistics , 2010.

Efficient Belief Propagation for Utility Maximization and Repeated Inference

A. Nath, P. DomingosAAAI Conference on Artificial Intelligence , 2010.

Approximate Lifted Belief Propagation

P. Singla, A. Nath, P. DomingosWorkshop on Statistical Relational AI , 2010.

Formula-Based Probabilistic Inference

V. Gogate, P. DomingosUncertainty in Artificial Intelligence , 2010.

Efficient Lifting for Online Probabilistic Inference

A. Nath, P. DomingosAAAI Conference on Artificial Intelligence , 2010.

Exploiting Logical Structure in Lifted Probabilistic Inference

V. Gogate, P. DomingosWorkshop on Statistical Relational AI , 2010.

Learning Markov Logic Networks Using Structural Motifs

S. Kok, P. DomingosInternational Conference on Machine Learning , 2010.

Bottom-Up Learning of Markov Network Structure

J. Davis, P. DomingosInternational 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. SchoenmackersAnnual Conference of the North American Chapter of the Association for Computational Linguistics , 2010.

Competitive Learning for Deep Temporal Networks

R. Gens, P. DomingosAnnual Conference on Neural Information Processing Systems , 2009.

Unsupervised Semantic Parsing

H. Poon, P. DomingosConference on Empirical Methods in Natural Language Processing , 2009.

A Language for Relational Decision Theory

A. Nath, P. DomingosInternational Workshop on Statistical Relational Learning , 2009.

Deep Transfer via Second-Order Markov Logic

J. Davis, P. DomingosInternational Conference on Machine Learning , 2009.

Learning Markov Logic Network Structure via Hypergraph Lifting

S. Kok, P. DomingosInternational Conference on Machine Learning , 2009.

Joint Unsupervised Coreference Resolution with Markov Logic

H. Poon, P. DomingosConference on Empirical Methods in Natural Language Processing , 2008.

A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC

H. Poon, P. Domingos, M. SumnerAAAI Conference on Artificial Intelligence , 2008.

Lifted First-Order Belief Propagation

P. Singla, P. DomingosAAAI Conference on Artificial Intelligence , 2008.

Hybrid Markov Logic Networks

J. Wang, P. DomingosAAAI Conference on Artificial Intelligence , 2008.

Learning Arithmetic Circuits

D. Lowd, P. DomingosUncertainty in Artificial Intelligence , 2008.

Extracting Semantic Networks from Text via Relational Clustering

S. Kok, P. DomingosEuropean Conference on Machine Learning , 2008.

Joint Inference in Information Extraction

H. Poon, P. DomingosAAAI Spring Symposium Series , 2007.

Recursive Random Fields

D. Lowd, P. DomingosInternational Joint Conference on Artificial Intelligence , 2007.

Statistical Predicate Invention

S. Kok, P. DomingosInternational Conference on Machine Learning , 2007.

Structured Machine Learning: Ten Problems for the Next Ten Years

P. DomingosInternational Conference on Inductive Logic Programming , 2007.

Markov Logic in Infinite Domains

P. Singla, P. DomingosUncertainty in Artificial Intelligence , 2007.

Efficient Weight Learning for Markov Logic Networks

D. Lowd, P. DomingosEuropean Conference on Principles and Practice of Knowledge Discovery in Databases , 2007.

Sound and Efficient Inference with Probabilistic and Deterministic Dependencies

H. Poon, P. DomingosAAAI Conference on Artificial Intelligence , 2006.

Unifying Logical and Statistical AI

P. Domingos, S. Kok, H. Poon, M. Richardson, P. SinglaAAAI Conference on Artificial Intelligence , 2006.

Memory-Efficient Inference in Relational Domains

P. Singla, P. DomingosAAAI Conference on Artificial Intelligence , 2006.

Markov Logic Networks

M. Richardson, P. DomingosMachine Learning Journal , 2006.

Entity Resolution with Markov Logic

P. Singla, P. DomingosIEEE International Conference on Data Mining , 2006.

Discriminative Training of Markov Logic Networks

P. Singla, P. DomingosAAAI Conference on Artificial Intelligence , 2005.

Naive Bayes Models for Probability Estimation

D. Lowd, P. DomingosInternational Conference on Machine Learning , 2005.

Object identification with attribute-mediated dependencies

P. Singla, P. DomingosEuropean Conference on Principles and Practice of Knowledge Discovery in Databases , 2005.

Mining social networks for viral marketing

P. DomingosIEEE Intelligent Systems , 2005.

Learning the Structure of Markov Logic Networks

S. Kok, P. DomingosInternational Conference on Machine Learning , 2005.

iMAP: Discovering Complex Semantic Matches between Database Schemas

R. Dhamankar, Y. Lee, A.H. Doan, A. Halevy, P. DomingosACM SIGMOD International Conference on Management of Data , 2004.

Multi-relational record linkage

P. Singla, P. DomingosKDD Workshop on Multi-Relational Data Mining , 2004.

Adversarial Classification

N. Dalvi, P. Domingos,  Mausam, S. Sanghai, D. VermaKnowledge Discovery and Data Mining , 2004.

Trust management for the Semantic Web

M. Richardson, R. Agrawal, P. DomingosInternational Semantic Web Conference , 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. WolfmanProc.¬†of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data , 2003.

Mining massive relational databases

G. Hulten, P. Domingos, Y. AbeInternational Workshop on Statistical Relational Learning , 2003.

Building large knowledge bases by mass collaboration

M. Richardson, P. DomingosInternational Conference on Knowledge Capture , 2003.

Learning with knowledge from multiple experts

M. Richardson, P. DomingosInternational Conference on Machine Learning , 2003.

Learning from Infinite Data in Finite Time

P. Domingos, G. HultenAnnual Conference on Neural Information Processing Systems , 2002.

Relational Markov models and their application to adaptive Web navigation

C. Anderson, P. Domingos, D. WeldKnowledge Discovery and Data Mining , 2002.

Mining Complex Models from Arbitrarily Large Databases in Constant Time

G. Hulten, P. DomingosKnowledge Discovery and Data Mining , 2002.

Mining knowledge-sharing sites for viral marketing

M. Richardson, P. DomingosKnowledge Discovery and Data Mining , 2002.

Learning to Map between Ontologies on the Semantic Web

A.H. Doan, J. Madhavan, P. Domingos, A. HalevyInternational World Wide Web Conference , 2002.

Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach

A.H. Doan, P. Domingos, A. HalevyACM SIGMOD International Conference on Management of Data , 2001.

A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering

P. Domingos, G. HultenInternational Conference on Machine Learning , 2001.

The intelligent surfer: Probabilistic combination of link and content information in PageRank

M. Richardson, P. DomingosAnnual Conference on Neural Information Processing Systems , 2001.

Mining the network value of customers

P. Domingos, M. RichardsonKnowledge Discovery and Data Mining , 2001.

Mining Time-Changing Data Streams

G. Hulten, P. Domingos, L. SpencerKnowledge Discovery and Data Mining , 2001.

Mining high-speed data streams

G. Hulten, P. DomingosKnowledge Discovery and Data Mining , 2000.