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ORCIDPedro M. Domingos
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- affiliation: University of Washington, Department of Computer Science & Engineering
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2020 – today
- 2021
[p7]Tarek R. Besold, Artur S. d'Avila Garcez, Sebastian Bader
, Howard Bowman, Pedro M. Domingos, Pascal Hitzler, Kai-Uwe Kühnberger, Luís C. Lamb, Priscila Machado Vieira Lima, Leo de Penning, Gadi Pinkas, Hoifung Poon, Gerson Zaverucha:
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. Neuro-Symbolic Artificial Intelligence 2021: 1-51
2010 – 2019
- 2019
- [j24]Pedro M. Domingos, Daniel Lowd:
Unifying logical and statistical AI with Markov logic. Commun. ACM 62(7): 74-83 (2019) - 2018
- [c129]Abram L. Friesen, Pedro M. Domingos:
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem. ICLR (Poster) 2018 - [c128]Abram L. Friesen, Pedro M. Domingos:
Submodular Field Grammars: Representation, Inference, and Application to Image Parsing. NeurIPS 2018: 4312-4322 - [c127]Pedro M. Domingos:
Machine Learning for Data Management: Problems and Solutions. SIGMOD Conference 2018: 629 - 2017
- [j23]Robert Peharz, Robert Gens, Franz Pernkopf, Pedro M. Domingos:
On the Latent Variable Interpretation in Sum-Product Networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(10): 2030-2044 (2017) - [c126]Robert Gens, Pedro M. Domingos:
Compositional Kernel Machines. ICLR (Workshop) 2017 - [i16]Abram L. Friesen, Pedro M. Domingos:
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem. CoRR abs/1710.11573 (2017) - [i15]Tarek R. Besold, Artur S. d'Avila Garcez, Sebastian Bader, Howard Bowman, Pedro M. Domingos, Pascal Hitzler, Kai-Uwe Kühnberger, Luís C. Lamb, Daniel Lowd, Priscila Machado Vieira Lima, Leo de Penning, Gadi Pinkas, Hoifung Poon, Gerson Zaverucha:
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. CoRR abs/1711.03902 (2017) - 2016
- [j22]Vasant Dhar, Pedro M. Domingos:
Pedro Domingos on The Master Algorithm: A Conversation with Vasant Dhar. Big Data 4(1): 10-13 (2016) - [j21]Vibhav Gogate, Pedro M. Domingos:
Probabilistic theorem proving. Commun. ACM 59(7): 107-115 (2016) - [c125]Aniruddh Nath, Pedro M. Domingos:
Learning Tractable Probabilistic Models for Fault Localization. AAAI 2016: 1294-1301 - [c124]Abram L. Friesen, Pedro M. Domingos:
The Sum-Product Theorem: A Foundation for Learning Tractable Models. ICML 2016: 1909-1918 - [c123]Pedro M. Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson, Parag Singla:
Unifying Logical and Statistical AI. LICS 2016: 1-11 - [p6]Geoff Hulten, Pedro M. Domingos:
Mining Decision Trees from Streams. Data Stream Management 2016: 189-208 - [i14]Robert Peharz, Robert Gens, Franz Pernkopf, Pedro M. Domingos:
On the Latent Variable Interpretation in Sum-Product Networks. CoRR abs/1601.06180 (2016) - [i13]Abram L. Friesen, Pedro M. Domingos:
Recursive Decomposition for Nonconvex Optimization. CoRR abs/1611.02755 (2016) - [i12]Abram L. Friesen, Pedro M. Domingos:
The Sum-Product Theorem: A Foundation for Learning Tractable Models. CoRR abs/1611.03553 (2016) - 2015
- [c122]Aniruddh Nath, Pedro M. Domingos:
Learning Relational Sum-Product Networks. AAAI 2015: 2878-2886 - [c121]Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro M. Domingos:
On Theoretical Properties of Sum-Product Networks. AISTATS 2015 - [c120]Abram L. Friesen, Pedro M. Domingos:
Recursive Decomposition for Nonconvex Optimization - IJCAI-15 Distinguished Paper. IJCAI 2015: 253-259 - [c119]Mathias Niepert, Pedro M. Domingos:
Learning and Inference in Tractable Probabilistic Knowledge Bases. UAI 2015: 632-641 - [i11]Aniruddh Nath, Pedro M. Domingos:
Learning Tractable Probabilistic Models for Fault Localization. CoRR abs/1507.01698 (2015) - 2014
- [c118]Aniruddh Nath, Pedro M. Domingos:
Automated Debugging with Tractable Probabilistic Programming. StarAI@AAAI 2014 - [c117]Aniruddh Nath, Pedro M. Domingos:
Learning Tractable Statistical Relational Models. StarAI@AAAI 2014 - [c116]Mathias Niepert, Pedro M. Domingos:
Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond. StarAI@AAAI 2014 - [c115]Parag Singla, Aniruddh Nath, Pedro M. Domingos:
Approximate Lifting Techniques for Belief Propagation. AAAI 2014: 2497-2504 - [c114]Mathias Niepert, Pedro M. Domingos:
Exchangeable Variable Models. ICML 2014: 271-279 - [c113]Robert Gens, Pedro M. Domingos:
Deep Symmetry Networks. NIPS 2014: 2537-2545 - [i10]Mathias Niepert, Pedro M. Domingos:
Exchangeable Variable Models. CoRR abs/1405.0501 (2014) - 2013
- [c112]William Austin Webb, Pedro M. Domingos:
Tractable Probabilistic Knowledge Bases with Existence Uncertainty. StarAI@AAAI 2013 - [c111]Robert Gens, Pedro M. Domingos:
Learning the Structure of Sum-Product Networks. ICML (3) 2013: 873-880 - [c110]Vibhav Gogate, Pedro M. Domingos:
Structured Message Passing. UAI 2013 - [i9]Vibhav Gogate, Pedro M. Domingos:
Structured Message Passing. CoRR abs/1309.6832 (2013) - 2012
- [j20]Pedro M. Domingos:
A few useful things to know about machine learning. Commun. ACM 55(10): 78-87 (2012) - [c109]Pedro M. Domingos, William Austin Webb:
A Tractable First-Order Probabilistic Logic. AAAI 2012: 1902-1909 - [c108]Chloé Kiddon, Pedro M. Domingos:
Knowledge Extraction and Joint Inference Using Tractable Markov Logic. AKBC-WEKEX@NAACL-HLT 2012: 79-83 - [c107]Robert Gens, Pedro M. Domingos:
Discriminative Learning of Sum-Product Networks. NIPS 2012: 3248-3256 - [i8]Vibhav Gogate, Pedro M. Domingos:
Approximation by Quantization. CoRR abs/1202.3723 (2012) - [i7]Vibhav Gogate, Pedro M. Domingos:
Probabilistic Theorem Proving. CoRR abs/1202.3724 (2012) - [i6]Hoifung Poon, Pedro M. Domingos:
Sum-Product Networks: A New Deep Architecture. CoRR abs/1202.3732 (2012) - [i5]Vibhav Gogate, Pedro M. Domingos:
Formula-Based Probabilistic Inference. CoRR abs/1203.3482 (2012) - [i4]Daniel Lowd, Pedro M. Domingos:
Learning Arithmetic Circuits. CoRR abs/1206.3271 (2012) - [i3]Parag Singla, Pedro M. Domingos:
Markov Logic in Infinite Domains. CoRR abs/1206.5292 (2012) - 2011
- [j19]Jesse Davis, Pedro M. Domingos:
Deep Transfer: A Markov Logic Approach. AI Mag. 32(1): 51-53 (2011) - [j18]Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro M. Domingos, Kristian Kersting, Xifeng Yan:
Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning. Mach. Learn. 83(2): 133-135 (2011) - [c106]Chloé Kiddon, Pedro M. Domingos:
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models. AAAI 2011: 1049-1056 - [c105]Hoifung Poon, Pedro M. Domingos:
Sum-product networks: A new deep architecture. ICCV Workshops 2011: 689-690 - [c104]James Blythe, Jerry R. Hobbs, Pedro M. Domingos, Rohit J. Kate, Raymond J. Mooney:
Implementing Weighted Abduction in Markov Logic. IWCS 2011 - [c103]Vibhav Gogate, Pedro M. Domingos:
Approximation by Quantization. UAI 2011: 247-255 - [c102]Vibhav Gogate, Pedro M. Domingos:
Probabilistic Theorem Proving. UAI 2011: 256-265 - [c101]Hoifung Poon, Pedro M. Domingos:
Sum-Product Networks: A New Deep Architecture. UAI 2011: 337-346 - [i2]Pedro M. Domingos, Sumit K. Sanghai, Daniel S. Weld:
Relational Dynamic Bayesian Networks. CoRR abs/1109.2137 (2011) - 2010
- [c100]Vibhav Gogate, Pedro M. Domingos:
Exploiting Logical Structure in Lifted Probabilistic Inference. StarAI@AAAI 2010 - [c99]Chloé Kiddon, Pedro M. Domingos:
Leveraging Ontologies for Lifted Probabilistic Inference and Learning. StarAI@AAAI 2010 - [c98]Stanley Kok, Pedro M. Domingos:
Using Structural Motifs for Learning Markov Logic Networks. StarAI@AAAI 2010 - [c97]Aniruddh Nath, Pedro M. Domingos:
Efficient Belief Propagation for Utility Maximization and Repeated Inference. AAAI 2010: 1187-1192 - [c96]Aniruddh Nath, Pedro M. Domingos:
Efficient Lifting for Online Probabilistic Inference. AAAI 2010: 1193-1198 - [c95]Aniruddh Nath, Pedro M. Domingos:
Efficient Lifting for Online Probabilistic Inference. StarAI@AAAI 2010 - [c94]Hoifung Poon, Pedro M. Domingos:
Machine Reading: A "Killer App" for Statistical Relational AI. StarAI@AAAI 2010 - [c93]Parag Singla, Aniruddh Nath, Pedro M. Domingos:
Approximate Lifted Belief Propagation. StarAI@AAAI 2010 - [c92]Hoifung Poon, Pedro M. Domingos:
Unsupervised Ontology Induction from Text. ACL 2010: 296-305 - [c91]Jesse Davis, Pedro M. Domingos:
Bottom-Up Learning of Markov Network Structure. ICML 2010: 271-278 - [c90]Stanley Kok, Pedro M. Domingos:
Learning Markov Logic Networks Using Structural Motifs. ICML 2010: 551-558 - [c89]Vibhav Gogate, William Austin Webb, Pedro M. Domingos:
Learning Efficient Markov Networks. NIPS 2010: 748-756 - [c88]Daniel Lowd, Pedro M. Domingos:
Approximate Inference by Compilation to Arithmetic Circuits. NIPS 2010: 1477-1485 - [c87]Vibhav Gogate, Pedro M. Domingos:
Formula-Based Probabilistic Inference. UAI 2010: 210-219 - [p5]Pedro M. Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson, Parag Singla:
Markov Logic: A Language and Algorithms for Link Mining. Link Mining 2010: 135-161
2000 – 2009
- 2009
- [b1]Pedro M. Domingos, Daniel Lowd:
Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2009, ISBN 978-3-031-00421-6 - [c86]Hoifung Poon, Pedro M. Domingos:
Unsupervised Semantic Parsing. EMNLP 2009: 1-10 - [c85]Jesse Davis, Pedro M. Domingos:
Deep transfer via second-order Markov logic. ICML 2009: 217-224 - [c84]Stanley Kok, Pedro M. Domingos:
Learning Markov logic network structure via hypergraph lifting. ICML 2009: 505-512 - 2008
- [j17]Thomas G. Dietterich, Pedro M. Domingos, Lise Getoor, Stephen H. Muggleton, Prasad Tadepalli:
Structured machine learning: the next ten years. Mach. Learn. 73(1): 3-23 (2008) - [c83]Hoifung Poon, Pedro M. Domingos, Marc Sumner:
A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC. AAAI 2008: 1075-1080 - [c82]Parag Singla, Pedro M. Domingos:
Lifted First-Order Belief Propagation. AAAI 2008: 1094-1099 - [c81]Jue Wang, Pedro M. Domingos:
Hybrid Markov Logic Networks. AAAI 2008: 1106-1111 - [c80]Pedro M. Domingos:
Markov logic: a unifying language for knowledge and information management. CIKM 2008: 519 - [c79]Hoifung Poon, Pedro M. Domingos:
Joint Unsupervised Coreference Resolution with Markov Logic. EMNLP 2008: 650-659 - [c78]Stanley Kok, Pedro M. Domingos:
Extracting Semantic Networks from Text Via Relational Clustering. ECML/PKDD (1) 2008: 624-639 - [c77]Pedro M. 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 - [c76]Pedro M. 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 - [c75]Daniel Lowd, Pedro M. Domingos:
Learning Arithmetic Circuits. UAI 2008: 383-392 - [p4]Pedro M. Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla:
Markov Logic. Probabilistic Inductive Logic Programming 2008: 92-117 - 2007
- [j16]Pedro M. Domingos:
Toward knowledge-rich data mining. Data Min. Knowl. Discov. 15(1): 21-28 (2007) - [c74]Hoifung Poon, Pedro M. Domingos:
Joint Inference in Information Extraction. AAAI 2007: 913-918 - [c73]Stanley Kok, Pedro M. Domingos:
Statistical predicate invention. ICML 2007: 433-440 - [c72]Daniel Lowd, Pedro M. Domingos:
Recursive Random Fields. IJCAI 2007: 950-955 - [c71]Daniel Lowd, Pedro M. Domingos:
Efficient Weight Learning for Markov Logic Networks. PKDD 2007: 200-211 - [c70]Parag Singla, Pedro M. Domingos:
Markov Logic in Infinite Domains. UAI 2007: 368-375 - [i1]Pedro M. Domingos, Parag Singla:
Markov Logic in Infinite Domains. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - 2006
- [j15]Matthew Richardson, Pedro M. Domingos:
Markov logic networks. Mach. Learn. 62(1-2): 107-136 (2006) - [c69]Pedro M. Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla:
Unifying Logical and Statistical AI. AAAI 2006: 2-9 - [c68]Hoifung Poon, Pedro M. Domingos:
Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. AAAI 2006: 458-463 - [c67]Parag Singla, Pedro M. Domingos:
Memory-Efficient Inference in Relational Domains. AAAI 2006: 488-493 - [c66]Pedro M. Domingos:
Learning, Logic, and Probability: A Unified View. EKAW 2006: 2 - [c65]Pedro M. Domingos:
Learning, Logic, and Probability: A Unified View. IBERAMIA-SBIA 2006: 3 - [c64]Parag Singla, Pedro M. Domingos:
Entity Resolution with Markov Logic. ICDM 2006: 572-582 - [c63]Pedro M. Domingos:
Learning, Logic, and Probability: A Unified View. PRICAI 2006: 1 - 2005
- [j14]Michael L. Anderson, Thomas Barkowsky, Pauline Berry, Douglas S. Blank, Timothy Chklovski, Pedro M. 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 Mag. 26(2): 87-92 (2005) - [j13]Steffen Staab, Pedro M. Domingos, Peter Mika, Jennifer Golbeck, Li Ding, Timothy W. Finin, Anupam Joshi, Andrzej Nowak, Robin R. Vallacher:
Social Networks Applied. IEEE Intell. Syst. 20(1): 80-93 (2005) - [c62]Parag Singla, Pedro M. Domingos:
Discriminative Training of Markov Logic Networks. AAAI 2005: 868-873 - [c61]Timothy Chklovski, Pedro M. Domingos, Henry Lieberman, Rada Mihalcea, Push Singh:
Organizing Committee. AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors 2005 - [c60]Pedro M. Domingos, Fernando M. Silva, Horácio C. Neto:
An Efficient and Scalable Architecture for Neural Networks with Backpropagation Learning. FPL 2005: 89-94 - [c59]Stanley Kok, Pedro M. Domingos:
Learning the structure of Markov logic networks. ICML 2005: 441-448 - [c58]Daniel Lowd, Pedro M. Domingos:
Naive Bayes models for probability estimation. ICML 2005: 529-536 - [c57]Parag Singla, Pedro M. Domingos:
Collective Object Identification. IJCAI 2005: 1636-1637 - [c56]Parag Singla, Pedro M. Domingos:
Object Identification with Attribute-Mediated Dependences. PKDD 2005: 297-308 - 2004
- [c55]Pedro M. Domingos:
Learning, Logic, and Probability: A Unified View. ALT 2004: 53 - [c54]Pedro M. Domingos:
Real-World Learning with Markov Logic Networks. ECML 2004: 17 - [c53]Daniel Grossman, Pedro M. Domingos:
Learning Bayesian network classifiers by maximizing conditional likelihood. ICML 2004 - [c52]Pedro M. Domingos:
Learning, Logic, and Probability: A Unified View. ILP 2004: 359 - [c51]Nilesh N. Dalvi, Pedro M. Domingos, Mausam, Sumit K. Sanghai, Deepak Verma:
Adversarial classification. KDD 2004: 99-108 - [c50]Pedro M. Domingos:
Real-World Learning with Markov Logic Networks. PKDD 2004: 17 - [c49]Robin Dhamankar, Yoonkyong Lee, AnHai Doan, Alon Y. Halevy, Pedro M. Domingos:
iMAP: Discovering Complex Mappings between Database Schemas. SIGMOD Conference 2004: 383-394 - [p3]AnHai Doan, Jayant Madhavan, Pedro M. Domingos, Alon Y. Halevy:
Ontology Matching: A Machine Learning Approach. Handbook on Ontologies 2004: 385-404 - [p2]Matthew Richardson, Pedro M. Domingos:
Combining Link and Content Information in Web Search. Web Dynamics 2004: 179-194 - 2003
- [j12]AnHai Doan, Pedro M. Domingos, Alon Y. Halevy:
Learning to Match the Schemas of Data Sources: A Multistrategy Approach. Mach. Learn. 50(3): 279-301 (2003) - [j11]Foster J. Provost, Pedro M. Domingos:
Tree Induction for Probability-Based Ranking. Mach. Learn. 52(3): 199-215 (2003) - [j10]Tessa A. Lau, Steven A. Wolfman, Pedro M. Domingos, Daniel S. Weld:
Programming by Demonstration Using Version Space Algebra. Mach. Learn. 53(1-2): 111-156 (2003) - [j9]Pedro M. Domingos:
Prospects and challenges for multi-relational data mining. SIGKDD Explor. 5(1): 80-83 (2003) - [j8]AnHai Doan, Jayant Madhavan, Robin Dhamankar, Pedro M. Domingos, Alon Y. Halevy:
Learning to match ontologies on the Semantic Web. VLDB J. 12(4): 303-319 (2003) - [c48]Pedro M. Domingos, Matthew Richardson:
Learning from Networks of Examples. EPIA 2003: 5 - [c47]Matthew Richardson, Pedro M. Domingos:
Learning with Knowledge from Multiple Experts. ICML 2003: 624-631 - [c46]Daniel S. Weld, Corin R. Anderson, Pedro M. Domingos, Oren Etzioni, Krzysztof Gajos, Tessa A. Lau, Steven A. Wolfman:
Automatically Personalizing User Interfaces. IJCAI 2003: 1613-1619 - [c45]Tessa A. Lau, Pedro M. Domingos, Daniel S. Weld:
Learning programs from traces using version space algebra. K-CAP 2003: 36-43 - [c44]Matthew Richardson, Pedro M. Domingos:
Building large knowledge bases by mass collaboration. K-CAP 2003: 129-137 - [c43]