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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]Matthew Richardson, Rakesh Agrawal, Pedro M. Domingos:
Trust Management for the Semantic Web. ISWC 2003: 351-368 - [e1]Lise Getoor, Ted E. Senator, Pedro M. Domingos, Christos Faloutsos:
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 [contents] - 2002
- [j7]Pedro M. Domingos:
When and How to Subsample: Report on the KDD-2001 Panel. SIGKDD Explor. 3(2): 74-75 (2002) - [c42]Jayant Madhavan, Philip A. Bernstein, Pedro M. Domingos, Alon Y. Halevy:
Representing and Reasoning about Mappings between Domain Models. AAAI/IAAI 2002: 80-86 - [c41]Matthew Richardson, Pedro M. Domingos:
Mining knowledge-sharing sites for viral marketing. KDD 2002: 61-70 - [c40]Corin R. Anderson, Pedro M. Domingos, Daniel S. Weld:
Relational Markov models and their application to adaptive web navigation. KDD 2002: 143-152 - [c39]Geoff Hulten, Pedro M. Domingos:
Mining complex models from arbitrarily large databases in constant time. KDD 2002: 525-531 - [c38]AnHai Doan, Jayant Madhavan, Pedro M. Domingos, Alon Y. Halevy:
Learning to map between ontologies on the semantic web. WWW 2002: 662-673 - 2001
- [c37]Pedro M. Domingos, Geoff Hulten:
Catching up with the Data: Research Issues in Mining Data Streams. DMKD 2001 - [c36]Pedro M. Domingos, Geoff Hulten:
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering. ICML 2001: 106-113 - [c35]Corin R. Anderson, Pedro M. Domingos, Daniel S. Weld:
Adaptive Web Navigation for Wireless Devices. IJCAI 2001: 879-884 - [c34]Steven A. Wolfman, Tessa A. Lau, Pedro M. Domingos, Daniel S. Weld:
Mixed initiative interfaces for learning tasks: SMARTedit talks back. IUI 2001: 167-174 - [c33]Pedro M. Domingos, Matthew Richardson:
Mining the network value of customers. KDD 2001: 57-66 - [c32]Geoff Hulten, Laurie Spencer, Pedro M. Domingos:
Mining time-changing data streams. KDD 2001: 97-106 - [c31]Pedro M. Domingos, Geoff Hulten:
Learning from Infinite Data in Finite Time. NIPS 2001: 673-680 - [c30]Matthew Richardson, Pedro M. Domingos:
The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank. NIPS 2001: 1441-1448 - [c29]AnHai Doan, Pedro M. Domingos, Alon Y. Halevy:
Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach. SIGMOD Conference 2001: 509-520 - [c28]Corin R. Anderson, Pedro M. Domingos, Daniel S. Weld:
Personalizing Web Sites for Mobile Users. WWW 2001: 565-575 - [p1]Tessa Lau, Steven A. Wolfman, Pedro M. Domingos, Daniel S. Weld:
Learning Repetitive Text-Editing Procedures with SMARTedit. Your Wish is My Command 2001: 209-226 - 2000
- [c27]Pedro M. Domingos:
A Unified Bias-Variance Decomposition for Zero-One and Squared Loss. AAAI/IAAI 2000: 564-569 - [c26]Pedro M. Domingos:
Beyond Occam's Razor: Process-Oriented Evaluation. ECML 2000: 3 - [c25]Pedro M. Domingos:
Bayesian Averaging of Classifiers and the Overfitting Problem. ICML 2000: 223-230 - [c24]Pedro M. Domingos:
A Unifeid Bias-Variance Decomposition and its Applications. ICML 2000: 231-238 - [c23]Tessa A. Lau, Pedro M. Domingos, Daniel S. Weld:
Version Space Algebra and its Application to Programming by Demonstration. ICML 2000: 527-534 - [c22]Pedro M. Domingos, Geoff Hulten:
Mining high-speed data streams. KDD 2000: 71-80 - [c21]AnHai Doan, Pedro M. Domingos, Alon Y. Levy:
Learning Source Description for Data Integration. WebDB (Informal Proceedings) 2000: 81-86
1990 – 1999
- 1999
- [j6]Pedro M. Domingos:
The Role of Occam's Razor in Knowledge Discovery. Data Min. Knowl. Discov. 3(4): 409-425 (1999) - [c20]Pedro M. Domingos:
Process-oriented evaluation: The next step. AISTATS 1999 - [c19]Pedro M. Domingos:
Process-Oriented Estimation of Generalization Error. IJCAI 1999: 714-721 - [c18]Pedro M. Domingos:
MetaCost: A General Method for Making Classifiers Cost-Sensitive. KDD 1999: 155-164 - 1998
- [j5]Pedro M. Domingos:
Knowledge Discovery Via Multiple Models. Intell. Data Anal. 2(1-4): 187-202 (1998) - [c17]Pedro M. Domingos:
A Process-Oriented Heuristic for Model Selection. ICML 1998: 127-135 - [c16]Pedro M. Domingos:
Occam's Two Razors: The Sharp and the Blunt. KDD 1998: 37-43 - 1997
- [j4]Pedro M. Domingos:
Control-Sensitive Feature Selection for Lazy Learners. Artif. Intell. Rev. 11(1-5): 227-253 (1997) - [j3]Pedro M. Domingos, Michael J. Pazzani:
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Mach. Learn. 29(2-3): 103-130 (1997) - [c15]Pedro M. Domingos:
A Comparison of Model Averaging Methods in Foreign Exchange Prediction. AAAI/IAAI 1997: 828 - [c14]Pedro M. Domingos:
Learning Multiple Models without Sacrificing Comprehensibility. AAAI/IAAI 1997: 829 - [c13]Pedro M. Domingos:
Knowledge Acquisition form Examples Vis Multiple Models. ICML 1997: 98-106 - [c12]Pedro M. Domingos:
Why Does Bagging Work? A Bayesian Account and its Implications. KDD 1997: 155-158 - 1996
- [j2]Pedro M. Domingos:
Two-Way Induction. Int. J. Artif. Intell. Tools 5(1-2): 113-126 (1996) - [j1]Pedro M. Domingos:
Unifying Instance-Based and Rule-Based Induction. Mach. Learn. 24(2): 141-168 (1996) - [c11]Pedro M. Domingos:
Towards a Unified Approach to Concept Learning. AAAI/IAAI, Vol. 2 1996: 1361 - [c10]Pedro M. Domingos:
Fast Discovery of Simple Rules. AAAI/IAAI, Vol. 2 1996: 1384 - [c9]Pedro M. Domingos:
Multistrategy Learning: A Case Study. AAAI/IAAI, Vol. 2 1996: 1385 - [c8]Pedro M. Domingos, Michael J. Pazzani:
Simple Bayesian Classifiers Do Not Assume Independence. AAAI/IAAI, Vol. 2 1996: 1386 - [c7]Pedro M. Domingos, Michael J. Pazzani:
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. ICML 1996: 105-112 - [c6]Pedro M. Domingos:
Linear-Time Rule Induction. KDD 1996: 96-101 - [c5]Pedro M. Domingos:
Efficient Specific-to-General Rule Induction. KDD 1996: 319-322 - 1995
- [c4]Pedro M. Domingos:
Two-way induction. ICTAI 1995: 182-189 - [c3]Pedro M. Domingos, Ernesto M. Morgado:
Progressive rules: a method for representing and using real-time knowledge. ICTAI 1995: 408-415 - [c2]Pedro M. Domingos:
Rule Induction and Instance-Based Learning: A Unified Approach. IJCAI 1995: 1226-1232 - 1994
- [c1]Pedro M. Domingos:
The RISE System: Conquering without Separating. ICTAI 1994: 704-707
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-07-07 00:30 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
