Pedro Domingos

Pedro Domingos: A few useful things to know about machine learning. Commun. ACM 55(10): 78-87 (2012)

Pedro Domingos, William Austin Webb: A Tractable First-Order Probabilistic Logic. AAAI 2012

Robert Gens, Pedro Domingos: Discriminative Learning of Sum-Product Networks. NIPS 2012: 3248-3256

Vibhav Gogate, Pedro Domingos: Approximation by Quantization. CoRR abs/1202.3723 (2012)

Vibhav Gogate, Pedro Domingos: Probabilistic Theorem Proving. CoRR abs/1202.3724 (2012)

Hoifung Poon, Pedro Domingos: Sum-Product Networks: A New Deep Architecture. CoRR abs/1202.3732 (2012)

Vibhav Gogate, Pedro Domingos: Formula-Based Probabilistic Inference. CoRR abs/1203.3482 (2012)

Daniel Lowd, Pedro Domingos: Learning Arithmetic Circuits. CoRR abs/1206.3271 (2012)

Parag Singla, Pedro Domingos: Markov Logic in Infinite Domains. CoRR abs/1206.5292 (2012)

Jesse Davis, Pedro Domingos: Deep Transfer: A Markov Logic Approach. AI Magazine 32(1): 51-53 (2011)

Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro Domingos, Kristian Kersting, Xifeng Yan: Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning. Machine Learning 83(2): 133-135 (2011)

Chloe Kiddon, Pedro Domingos: Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models. AAAI 2011

Hoifung Poon, Pedro Domingos: Sum-product networks: A new deep architecture. ICCV Workshops 2011: 689-690

Vibhav Gogate, Pedro Domingos: Approximation by Quantization. UAI 2011: 247-255

Vibhav Gogate, Pedro Domingos: Probabilistic Theorem Proving. UAI 2011: 256-265

Hoifung Poon, Pedro Domingos: Sum-Product Networks: A New Deep Architecture. UAI 2011: 337-346

Pedro Domingos, Sumit K. Sanghai, Daniel S. Weld: Relational Dynamic Bayesian Networks. CoRR abs/1109.2137 (2011)

Vibhav Gogate, Pedro Domingos: Exploiting Logical Structure in Lifted Probabilistic Inference. Statistical Relational Artificial Intelligence 2010

Chloe Kiddon, Pedro Domingos: Leveraging Ontologies for Lifted Probabilistic Inference and Learning. Statistical Relational Artificial Intelligence 2010

Stanley Kok, Pedro Domingos: Using Structural Motifs for Learning Markov Logic Networks. Statistical Relational Artificial Intelligence 2010

Aniruddh Nath, Pedro Domingos: Efficient Belief Propagation for Utility Maximization and Repeated Inference. AAAI 2010

Aniruddh Nath, Pedro Domingos: Efficient Lifting for Online Probabilistic Inference. AAAI 2010

Aniruddh Nath, Pedro Domingos: Efficient Lifting for Online Probabilistic Inference. Statistical Relational Artificial Intelligence 2010

Hoifung Poon, Pedro Domingos: Machine Reading: A "Killer App" for Statistical Relational AI. Statistical Relational Artificial Intelligence 2010

Parag Singla, Aniruddh Nath, Pedro Domingos: Approximate Lifted Belief Propagation. Statistical Relational Artificial Intelligence 2010

Hoifung Poon, Pedro Domingos: Unsupervised Ontology Induction from Text. ACL 2010: 296-305

Jesse Davis, Pedro Domingos: Bottom-Up Learning of Markov Network Structure. ICML 2010: 271-278

Stanley Kok, Pedro Domingos: Learning Markov Logic Networks Using Structural Motifs. ICML 2010: 551-558

Vibhav Gogate, William Austin Webb, Pedro Domingos: Learning Efficient Markov Networks. NIPS 2010: 748-756

Daniel Lowd, Pedro Domingos: Approximate Inference by Compilation to Arithmetic Circuits. NIPS 2010: 1477-1485

Vibhav Gogate, Pedro Domingos: Formula-Based Probabilistic Inference. UAI 2010: 210-219

Pedro Domingos, Daniel Lowd: Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2009

Hoifung Poon, Pedro Domingos: Unsupervised Semantic Parsing. EMNLP 2009: 1-10

Jesse Davis, Pedro Domingos: Deep transfer via second-order Markov logic. ICML 2009: 28

Stanley Kok, Pedro Domingos: Learning Markov logic network structure via hypergraph lifting. ICML 2009: 64

Thomas G. Dietterich, Pedro Domingos, Lise Getoor, Stephen Muggleton, Prasad Tadepalli: Structured machine learning: the next ten years. Machine Learning 73(1): 3-23 (2008)

Hoifung Poon, Pedro Domingos, Marc Sumner: A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC. AAAI 2008: 1075-1080

Parag Singla, Pedro Domingos: Lifted First-Order Belief Propagation. AAAI 2008: 1094-1099

Jue Wang, Pedro Domingos: Hybrid Markov Logic Networks. AAAI 2008: 1106-1111

Pedro Domingos: Markov logic: a unifying language for knowledge and information management. CIKM 2008: 519

Hoifung Poon, Pedro Domingos: Joint Unsupervised Coreference Resolution with Markov Logic. EMNLP 2008: 650-659

Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla: Markov Logic. Probabilistic Inductive Logic Programming 2008: 92-117

Stanley Kok, Pedro Domingos: Extracting Semantic Networks from Text Via Relational Clustering. ECML/PKDD (1) 2008: 624-639

Pedro Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla: Just Add Weights: Markov Logic for the Semantic Web. URSW (LNCS Vol.) 2008: 1-25

Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla, Marc Sumner, Jue Wang: Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition. SSPR/SPR 2008: 3

Daniel Lowd, Pedro Domingos: Learning Arithmetic Circuits. UAI 2008: 383-392

Pedro Domingos: Toward knowledge-rich data mining. Data Min. Knowl. Discov. 15(1): 21-28 (2007)

Hoifung Poon, Pedro Domingos: Joint Inference in Information Extraction. AAAI 2007: 913-918

Pedro Domingos, Parag Singla: Markov Logic in Infinite Domains. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007

Stanley Kok, Pedro Domingos: Statistical predicate invention. ICML 2007: 433-440

Daniel Lowd, Pedro Domingos: Recursive Random Fields. IJCAI 2007: 950-955

Daniel Lowd, Pedro Domingos: Efficient Weight Learning for Markov Logic Networks. PKDD 2007: 200-211

Parag Singla, Pedro Domingos: Markov Logic in Infinite Domains. UAI 2007: 368-375

Matthew Richardson, Pedro Domingos: Markov logic networks. Machine Learning 62(1-2): 107-136 (2006)

Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla: Unifying Logical and Statistical AI. AAAI 2006: 2-9

Hoifung Poon, Pedro Domingos: Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. AAAI 2006: 458-463

Parag Singla, Pedro Domingos: Memory-Efficient Inference in Relational Domains. AAAI 2006: 488-493

Pedro Domingos: Learning, Logic, and Probability: A Unified View. EKAW 2006: 2

Pedro Domingos: Learning, Logic, and Probability: A Unified View. IBERAMIA-SBIA 2006: 3

Parag Singla, Pedro Domingos: Entity Resolution with Markov Logic. ICDM 2006: 572-582

Pedro Domingos: Learning, Logic, and Probability: A Unified View. PRICAI 2006: 1

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 L. Popp, Daniel G. Shapiro, Nathan Schurr, Push Singh, John Yen: Reports on the 2005 AAAI Spring Symposium Series. AI Magazine 26(2): 87-92 (2005)

Steffen Staab, Pedro Domingos, Peter Mika, Jennifer Golbeck, Li Ding, Timothy W. Finin, Anupam Joshi, Andrzej Nowak, Robin R. Vallacher: Social Networks Applied. IEEE Intelligent Systems 20(1): 80-93 (2005)

Parag Singla, Pedro Domingos: Discriminative Training of Markov Logic Networks. AAAI 2005: 868-873

Timothy Chklovski, Pedro Domingos, Henry Lieberman, Rada Mihalcea, Push Singh: Organizing Committee. AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors 2005

Pedro Domingos, Fernando M. Silva, Horácio C. Neto: An Efficient and Scalable Architecture for Neural Networks with Backpropagation Learning. FPL 2005: 89-94

Stanley Kok, Pedro Domingos: Learning the structure of Markov logic networks. ICML 2005: 441-448

Daniel Lowd, Pedro Domingos: Naive Bayes models for probability estimation. ICML 2005: 529-536

Parag Singla, Pedro Domingos: Collective Object Identification. IJCAI 2005: 1636-1637

Parag Singla, Pedro Domingos: Object Identification with Attribute-Mediated Dependences. PKDD 2005: 297-308

AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Y. Halevy: Ontology Matching: A Machine Learning Approach. Handbook on Ontologies 2004: 385-404

Matthew Richardson, Pedro Domingos: Combining Link and Content Information in Web Search. Web Dynamics 2004: 179-194

Pedro Domingos: Learning, Logic, and Probability: A Unified View. ALT 2004: 53

Pedro Domingos: Real-World Learning with Markov Logic Networks. ECML 2004: 17

Daniel Grossman, Pedro Domingos: Learning Bayesian network classifiers by maximizing conditional likelihood. ICML 2004

Pedro Domingos: Learning, Logic, and Probability: A Unified View. ILP 2004: 359

Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. Sanghai, Deepak Verma: Adversarial classification. KDD 2004: 99-108

Pedro Domingos: Real-World Learning with Markov Logic Networks. PKDD 2004: 17

Robin Dhamankar, Yoonkyong Lee, AnHai Doan, Alon Y. Halevy, Pedro Domingos: iMAP: Discovering Complex Mappings between Database Schemas. SIGMOD Conference 2004: 383-394

AnHai Doan, Pedro Domingos, Alon Y. Halevy: Learning to Match the Schemas of Data Sources: A Multistrategy Approach. Machine Learning 50(3): 279-301 (2003)

Foster J. Provost, Pedro Domingos: Tree Induction for Probability-Based Ranking. Machine Learning 52(3): 199-215 (2003)

Tessa A. Lau, Steven A. Wolfman, Pedro Domingos, Daniel S. Weld: Programming by Demonstration Using Version Space Algebra. Machine Learning 53(1-2): 111-156 (2003)

Pedro Domingos: Prospects and challenges for multi-relational data mining. SIGKDD Explorations 5(1): 80-83 (2003)

AnHai Doan, Jayant Madhavan, Robin Dhamankar, Pedro Domingos, Alon Y. Halevy: Learning to match ontologies on the Semantic Web. VLDB J. 12(4): 303-319 (2003)

Pedro Domingos, Matthew Richardson: Learning from Networks of Examples. EPIA 2003: 5

Matthew Richardson, Pedro Domingos: Learning with Knowledge from Multiple Experts. ICML 2003: 624-631

Daniel S. Weld, Corin R. Anderson, Pedro Domingos, Oren Etzioni, Krzysztof Gajos, Tessa A. Lau, Steven A. Wolfman: Automatically Personalizing User Interfaces. IJCAI 2003: 1613-1619

Tessa A. Lau, Pedro Domingos, Daniel S. Weld: Learning programs from traces using version space algebra. K-CAP 2003: 36-43

Matthew Richardson, Pedro Domingos: Building large knowledge bases by mass collaboration. K-CAP 2003: 129-137

Matthew Richardson, Rakesh Agrawal, Pedro Domingos: Trust Management for the Semantic Web. International Semantic Web Conference 2003: 351-368

Lise Getoor, Ted E. Senator, Pedro Domingos, Christos Faloutsos (Eds.): Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, 2003. ACM 2003, isbn 1-58113-737-0

Pedro Domingos: When and How to Subsample: Report on the KDD-2001 Panel. SIGKDD Explorations 3(2): 74-75 (2002)

Jayant Madhavan, Philip A. Bernstein, Pedro Domingos, Alon Y. Halevy: Representing and Reasoning about Mappings between Domain Models. AAAI/IAAI 2002: 80-86

Matthew Richardson, Pedro Domingos: Mining knowledge-sharing sites for viral marketing. KDD 2002: 61-70

Corin R. Anderson, Pedro Domingos, Daniel S. Weld: Relational Markov models and their application to adaptive web navigation. KDD 2002: 143-152

Geoff Hulten, Pedro Domingos: Mining complex models from arbitrarily large databases in constant time. KDD 2002: 525-531

AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Y. Halevy: Learning to map between ontologies on the semantic web. WWW 2002: 662-673

Pedro Domingos, Geoff Hulten: Catching up with the Data: Research Issues in Mining Data Streams. DMKD 2001

Pedro Domingos, Geoff Hulten: A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering. ICML 2001: 106-113

Corin R. Anderson, Pedro Domingos, Daniel S. Weld: Adaptive Web Navigation for Wireless Devices. IJCAI 2001: 879-884

Steven A. Wolfman, Tessa A. Lau, Pedro Domingos, Daniel S. Weld: Mixed initiative interfaces for learning tasks: SMARTedit talks back. IUI 2001: 167-174

Pedro Domingos, Matthew Richardson: Mining the network value of customers. KDD 2001: 57-66

Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. KDD 2001: 97-106

Pedro Domingos, Geoff Hulten: Learning from Infinite Data in Finite Time. NIPS 2001: 673-680

Matthew Richardson, Pedro Domingos: The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank. NIPS 2001: 1441-1448

AnHai Doan, Pedro Domingos, Alon Y. Halevy: Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach. SIGMOD Conference 2001: 509-520

Corin R. Anderson, Pedro Domingos, Daniel S. Weld: Personalizing Web Sites for Mobile Users. WWW 2001: 565-575

Pedro Domingos: A Unified Bias-Variance Decomposition for Zero-One and Squared Loss. AAAI/IAAI 2000: 564-569

Pedro Domingos: Beyond Occam's Razor: Process-Oriented Evaluation. ECML 2000: 3

Pedro Domingos: Bayesian Averaging of Classifiers and the Overfitting Problem. ICML 2000: 223-230

Pedro Domingos: A Unifeid Bias-Variance Decomposition and its Applications. ICML 2000: 231-238

Tessa A. Lau, Pedro Domingos, Daniel S. Weld: Version Space Algebra and its Application to Programming by Demonstration. ICML 2000: 527-534

Pedro Domingos, Geoff Hulten: Mining high-speed data streams. KDD 2000: 71-80

AnHai Doan, Pedro Domingos, Alon Y. Levy: Learning Source Description for Data Integration. WebDB (Informal Proceedings) 2000: 81-86

Pedro Domingos: The Role of Occam's Razor in Knowledge Discovery. Data Min. Knowl. Discov. 3(4): 409-425 (1999)

Pedro Domingos: Process-Oriented Estimation of Generalization Error. IJCAI 1999: 714-721

Pedro Domingos: MetaCost: A General Method for Making Classifiers Cost-Sensitive. KDD 1999: 155-164

Pedro Domingos: Knowledge Discovery Via Multiple Models. Intell. Data Anal. 2(1-4): 187-202 (1998)

Pedro Domingos: A Process-Oriented Heuristic for Model Selection. ICML 1998: 127-135

Pedro Domingos: Occam's Two Razors: The Sharp and the Blunt. KDD 1998: 37-43

Pedro Domingos: Control-Sensitive Feature Selection for Lazy Learners. Artif. Intell. Rev. 11(1-5): 227-253 (1997)

Pedro Domingos, Michael J. Pazzani: On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning 29(2-3): 103-130 (1997)

Pedro Domingos: A Comparison of Model Averaging Methods in Foreign Exchange Prediction. AAAI/IAAI 1997: 828

Pedro Domingos: Learning Multiple Models without Sacrificing Comprehensibility. AAAI/IAAI 1997: 829

Pedro Domingos: Knowledge Acquisition form Examples Vis Multiple Models. ICML 1997: 98-106

Pedro Domingos: Why Does Bagging Work? A Bayesian Account and its Implications. KDD 1997: 155-158

Pedro Domingos: Unifying Instance-Based and Rule-Based Induction. Machine Learning 24(2): 141-168 (1996)

Pedro Domingos: Towards a Unified Approach to Concept Learning. AAAI/IAAI, Vol. 2 1996: 1361

Pedro Domingos: Fast Discovery of Simple Rules. AAAI/IAAI, Vol. 2 1996: 1384

Pedro Domingos: Multistrategy Learning: A Case Study. AAAI/IAAI, Vol. 2 1996: 1385

Pedro Domingos, Michael J. Pazzani: Simple Bayesian Classifiers Do Not Assume Independence. AAAI/IAAI, Vol. 2 1996: 1386

Pedro Domingos, Michael J. Pazzani: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. ICML 1996: 105-112

Pedro Domingos: Linear-Time Rule Induction. KDD 1996: 96-101

Pedro Domingos: Efficient Specific-to-General Rule Induction. KDD 1996: 319-322

Pedro Domingos: Rule Induction and Instance-Based Learning: A Unified Approach. IJCAI 1995: 1226-1232

Pedro Domingos: The RISE System: Conquering without Separating. ICTAI 1994: 704-707