| 2018 | ICLR | Deep Learning as a Mixed Convex-Combinatorial Optimization Problem. | Abram L. Friesen, Pedro M. Domingos |
| 2018 | SIGMOD | Machine Learning for Data Management: Problems and Solutions. | Pedro M. Domingos |
| 2017 | ICLR | Compositional Kernel Machines. | Robert Gens, Pedro M. Domingos |
| 2016 | AAAI | Learning Tractable Probabilistic Models for Fault Localization. | Aniruddh Nath, Pedro M. Domingos |
| 2016 | ICML | The Sum-Product Theorem: A Foundation for Learning Tractable Models. | Abram L. Friesen, Pedro M. Domingos |
| 2016 | LICS | Unifying Logical and Statistical AI. | Pedro M. Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson, Parag Singla |
| 2015 | AAAI | Learning Relational Sum-Product Networks. | Aniruddh Nath, Pedro M. Domingos |
| 2015 | AISTATS | On Theoretical Properties of Sum-Product Networks. | Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro M. Domingos |
| 2015 | IJCAI | Recursive Decomposition for Nonconvex Optimization - IJCAI-15 Distinguished Paper. | Abram L. Friesen, Pedro M. Domingos |
| 2015 | UAI | Learning and Inference in Tractable Probabilistic Knowledge Bases. | Mathias Niepert, Pedro M. Domingos |
| 2014 | AAAI | Automated Debugging with Tractable Probabilistic Programming. | Aniruddh Nath, Pedro M. Domingos |
| 2014 | AAAI | Learning Tractable Statistical Relational Models. | Aniruddh Nath, Pedro M. Domingos |
| 2014 | AAAI | Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond. | Mathias Niepert, Pedro M. Domingos |
| 2014 | AAAI | Approximate Lifting Techniques for Belief Propagation. | Parag Singla, Aniruddh Nath, Pedro M. Domingos |
| 2014 | ICML | Exchangeable Variable Models. | Mathias Niepert, Pedro M. Domingos |
| 2013 | AAAI | Tractable Probabilistic Knowledge Bases with Existence Uncertainty. | William Austin Webb, Pedro M. Domingos |
| 2013 | ICML | Learning the Structure of Sum-Product Networks. | Robert Gens, Pedro M. Domingos |
| 2013 | UAI | Structured Message Passing. | Vibhav Gogate, Pedro M. Domingos |
| 2012 | AAAI | A Tractable First-Order Probabilistic Logic. | Pedro M. Domingos, William Austin Webb |
| 2012 | NAACL | Knowledge Extraction and Joint Inference Using Tractable Markov Logic. | Chlo Kiddon, Pedro M. Domingos |
| 2011 | AAAI | Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models. | Chlo Kiddon, Pedro M. Domingos |
| 2011 | UAI | Approximation by Quantization. | Vibhav Gogate, Pedro M. Domingos |
| 2011 | UAI | Probabilistic Theorem Proving. | Vibhav Gogate, Pedro M. Domingos |
| 2011 | UAI | Sum-Product Networks: A New Deep Architecture. | Hoifung Poon, Pedro M. Domingos |
| 2010 | AAAI | Exploiting Logical Structure in Lifted Probabilistic Inference. | Vibhav Gogate, Pedro M. Domingos |
| 2010 | AAAI | Leveraging Ontologies for Lifted Probabilistic Inference and Learning. | Chlo Kiddon, Pedro M. Domingos |
| 2010 | AAAI | Using Structural Motifs for Learning Markov Logic Networks. | Stanley Kok, Pedro M. Domingos |
| 2010 | AAAI | Efficient Belief Propagation for Utility Maximization and Repeated Inference. | Aniruddh Nath, Pedro M. Domingos |
| 2010 | AAAI | Efficient Lifting for Online Probabilistic Inference. | Aniruddh Nath, Pedro M. Domingos |
| 2010 | AAAI | Efficient Lifting for Online Probabilistic Inference. | Aniruddh Nath, Pedro M. Domingos |
| 2010 | AAAI | Machine Reading: A "Killer App" for Statistical Relational AI. | Hoifung Poon, Pedro M. Domingos |
| 2010 | AAAI | Approximate Lifted Belief Propagation. | Parag Singla, Aniruddh Nath, Pedro M. Domingos |
| 2010 | ACL | Unsupervised Ontology Induction from Text. | Hoifung Poon, Pedro M. Domingos |
| 2010 | ICML | Bottom-Up Learning of Markov Network Structure. | Jesse Davis, Pedro M. Domingos |
| 2010 | ICML | Learning Markov Logic Networks Using Structural Motifs. | Stanley Kok, Pedro M. Domingos |
| 2010 | UAI | Formula-Based Probabilistic Inference. | Vibhav Gogate, Pedro M. Domingos |
| 2009 | EMNLP | Unsupervised Semantic Parsing. | Hoifung Poon, Pedro M. Domingos |
| 2009 | ICML | Deep transfer via second-order Markov logic. | Jesse Davis, Pedro M. Domingos |
| 2009 | ICML | Learning Markov logic network structure via hypergraph lifting. | Stanley Kok, Pedro M. Domingos |
| 2008 | AAAI | A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC. | Hoifung Poon, Pedro M. Domingos, Marc Sumner |
| 2008 | AAAI | Lifted First-Order Belief Propagation. | Parag Singla, Pedro M. Domingos |
| 2008 | AAAI | Hybrid Markov Logic Networks. | Jue Wang, Pedro M. Domingos |
| 2008 | CIKM | Markov logic: a unifying language for knowledge and information management. | Pedro M. Domingos |
| 2008 | EMNLP | Joint Unsupervised Coreference Resolution with Markov Logic. | Hoifung Poon, Pedro M. Domingos |
| 2008 | UAI | Learning Arithmetic Circuits. | Daniel Lowd, Pedro M. Domingos |
| 2008 | SSPR | Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition. | Pedro M. Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla, Marc Sumner, Jue Wang |
| 2007 | AAAI | Joint Inference in Information Extraction. | Hoifung Poon, Pedro M. Domingos |
| 2007 | ICML | Statistical predicate invention. | Stanley Kok, Pedro M. Domingos |
| 2007 | IJCAI | Recursive Random Fields. | Daniel Lowd, Pedro M. Domingos |
| 2007 | UAI | Markov Logic in Infinite Domains. | Parag Singla, Pedro M. Domingos |
| 2006 | AAAI | Unifying Logical and Statistical AI. | Pedro M. Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla |
| 2006 | AAAI | Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. | Hoifung Poon, Pedro M. Domingos |
| 2006 | AAAI | Memory-Efficient Inference in Relational Domains. | Parag Singla, Pedro M. Domingos |
| 2006 | EKAW | Learning, Logic, and Probability: A Unified View. | Pedro M. Domingos |
| 2006 | ICDM | Entity Resolution with Markov Logic. | Parag Singla, Pedro M. Domingos |
| 2006 | PRICAI | Learning, Logic, and Probability: A Unified View. | Pedro M. Domingos |
| 2005 | AAAI | Discriminative Training of Markov Logic Networks. | Parag Singla, Pedro M. Domingos |
| 2005 | FPL | An Efficient and Scalable Architecture for Neural Networks with Backpropagation Learning. | Pedro M. Domingos, Fernando M. Silva, Horcio C. Neto |
| 2005 | ICML | Learning the structure of Markov logic networks. | Stanley Kok, Pedro M. Domingos |
| 2005 | ICML | Naive Bayes models for probability estimation. | Daniel Lowd, Pedro M. Domingos |
| 2005 | IJCAI | Collective Object Identification. | Parag Singla, Pedro M. Domingos |
| 2004 | ALT | Learning, Logic, and Probability: A Unified View. | Pedro M. Domingos |
| 2004 | ICML | Learning Bayesian network classifiers by maximizing conditional likelihood. | Daniel Grossman, Pedro M. Domingos |
| 2004 | ILP | Learning, Logic, and Probability: A Unified View. | Pedro M. Domingos |
| 2004 | KDD | Adversarial classification. | Nilesh N. Dalvi, Pedro M. Domingos, Mausam, Sumit K. Sanghai, Deepak Verma |
| 2004 | SIGMOD | iMAP: Discovering Complex Mappings between Database Schemas. | Robin Dhamankar, Yoonkyong Lee, AnHai Doan, Alon Y. Halevy, Pedro M. Domingos |
| 2003 | EPIA | Learning from Networks of Examples. | Pedro M. Domingos, Matthew Richardson |
| 2003 | ICML | Learning with Knowledge from Multiple Experts. | Matthew Richardson, Pedro M. Domingos |
| 2003 | IJCAI | Automatically Personalizing User Interfaces. | Daniel S. Weld, Corin R. Anderson, Pedro M. Domingos, Oren Etzioni, Krzysztof Gajos, Tessa A. Lau, Steven A. Wolfman |
| 2002 | AAAI | Representing and Reasoning about Mappings between Domain Models. | Jayant Madhavan, Philip A. Bernstein, Pedro M. Domingos, Alon Y. Halevy |
| 2002 | KDD | Relational Markov models and their application to adaptive web navigation. | Corin R. Anderson, Pedro M. Domingos, Daniel S. Weld |
| 2002 | KDD | Mining complex models from arbitrarily large databases in constant time. | Geoff Hulten, Pedro M. Domingos |
| 2002 | KDD | Mining knowledge-sharing sites for viral marketing. | Matthew Richardson, Pedro M. Domingos |
| 2002 | WWW | Learning to map between ontologies on the semantic web. | AnHai Doan, Jayant Madhavan, Pedro M. Domingos, Alon Y. Halevy |
| 2001 | ICML | A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering. | Pedro M. Domingos, Geoff Hulten |
| 2001 | IJCAI | Adaptive Web Navigation for Wireless Devices. | Corin R. Anderson, Pedro M. Domingos, Daniel S. Weld |
| 2001 | IUI | Mixed initiative interfaces for learning tasks: SMARTedit talks back. | Steven A. Wolfman, Tessa A. Lau, Pedro M. Domingos, Daniel S. Weld |
| 2001 | KDD | Mining the network value of customers. | Pedro M. Domingos, Matthew Richardson |
| 2001 | KDD | Mining time-changing data streams. | Geoff Hulten, Laurie Spencer, Pedro M. Domingos |
| 2001 | WWW | Personalizing Web Sites for Mobile Users. | Corin R. Anderson, Pedro M. Domingos, Daniel S. Weld |
| 2001 | SIGMOD | Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach. | AnHai Doan, Pedro M. Domingos, Alon Y. Halevy |
| 2000 | AAAI | A Unified Bias-Variance Decomposition for Zero-One and Squared Loss. | Pedro M. Domingos |
| 2000 | ICML | Bayesian Averaging of Classifiers and the Overfitting Problem. | Pedro M. Domingos |
| 2000 | ICML | A Unifeid Bias-Variance Decomposition and its Applications. | Pedro M. Domingos |
| 2000 | ICML | Version Space Algebra and its Application to Programming by Demonstration. | Tessa A. Lau, Pedro M. Domingos, Daniel S. Weld |
| 2000 | KDD | Mining high-speed data streams. | Pedro M. Domingos, Geoff Hulten |
| 1999 | AISTATS | Process-oriented evaluation: The next step. | Pedro M. Domingos |
| 1999 | IJCAI | Process-Oriented Estimation of Generalization Error. | Pedro M. Domingos |
| 1999 | KDD | MetaCost: A General Method for Making Classifiers Cost-Sensitive. | Pedro M. Domingos |
| 1998 | ICML | A Process-Oriented Heuristic for Model Selection. | Pedro M. Domingos |
| 1998 | KDD | Occam's Two Razors: The Sharp and the Blunt. | Pedro M. Domingos |
| 1997 | AAAI | A Comparison of Model Averaging Methods in Foreign Exchange Prediction. | Pedro M. Domingos |
| 1997 | AAAI | Learning Multiple Models without Sacrificing Comprehensibility. | Pedro M. Domingos |
| 1997 | ICML | Knowledge Acquisition form Examples Vis Multiple Models. | Pedro M. Domingos |
| 1997 | KDD | Why Does Bagging Work? A Bayesian Account and its Implications. | Pedro M. Domingos |
| 1996 | AAAI | Towards a Unified Approach to Concept Learning. | Pedro M. Domingos |
| 1996 | AAAI | Fast Discovery of Simple Rules. | Pedro M. Domingos |
| 1996 | AAAI | Multistrategy Learning: A Case Study. | Pedro M. Domingos |
| 1996 | AAAI | Simple Bayesian Classifiers Do Not Assume Independence. | Pedro M. Domingos, Michael J. Pazzani |
| 1996 | ICML | Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. | Pedro M. Domingos, Michael J. Pazzani |
| 1996 | KDD | Linear-Time Rule Induction. | Pedro M. Domingos |
| 1996 | KDD | Efficient Specific-to-General Rule Induction. | Pedro M. Domingos |
| 1995 | IJCAI | Rule Induction and Instance-Based Learning: A Unified Approach. | Pedro M. Domingos |
| 1995 | ICTAI | Two-way induction. | Pedro M. Domingos |
| 1995 | ICTAI | Progressive rules: a method for representing and using real-time knowledge. | Pedro M. Domingos, Ernesto M. Morgado |
| 1994 | ICTAI | The RISE System: Conquering without Separating. | Pedro M. Domingos |