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Pedro Domingos Vis

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*2009
101EEJesse Davis, Pedro Domingos: Deep transfer via second-order Markov logic. ICML 2009: 28
100EEStanley Kok, Pedro Domingos: Learning Markov logic network structure via hypergraph lifting. ICML 2009: 64
2008
99 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
98 Parag Singla, Pedro Domingos: Lifted First-Order Belief Propagation. AAAI 2008: 1094-1099
97 Jue Wang, Pedro Domingos: Hybrid Markov Logic Networks. AAAI 2008: 1106-1111
96EEPedro Domingos: Markov logic: a unifying language for knowledge and information management. CIKM 2008: 519
95EEStanley Kok, Pedro Domingos: Extracting Semantic Networks from Text Via Relational Clustering. ECML/PKDD (1) 2008: 624-639
94EEHoifung Poon, Pedro Domingos: Joint Unsupervised Coreference Resolution with Markov Logic. EMNLP 2008: 650-659
93EEPedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla: Markov Logic. Probabilistic Inductive Logic Programming 2008: 92-117
92EEPedro 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
91EEDaniel Lowd, Pedro Domingos: Learning Arithmetic Circuits. UAI 2008: 383-392
90EEPedro 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
89EEThomas G. Dietterich, Pedro Domingos, Lise Getoor, Stephen Muggleton, Prasad Tadepalli: Structured machine learning: the next ten years. Machine Learning 73(1): 3-23 (2008)
2007
88 Hoifung Poon, Pedro Domingos: Joint Inference in Information Extraction. AAAI 2007: 913-918
87EEStanley Kok, Pedro Domingos: Statistical predicate invention. ICML 2007: 433-440
86EEDaniel Lowd, Pedro Domingos: Recursive Random Fields. IJCAI 2007: 950-955
85EEDaniel Lowd, Pedro Domingos: Efficient Weight Learning for Markov Logic Networks. PKDD 2007: 200-211
84EEPedro Domingos, Parag Singla: Markov Logic in Infinite Domains. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007
83EEPedro Domingos: Toward knowledge-rich data mining. Data Min. Knowl. Discov. 15(1): 21-28 (2007)
2006
82 Parag Singla, Pedro Domingos: Memory-Efficient Inference in Relational Domains. AAAI 2006
81 Hoifung Poon, Pedro Domingos: Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. AAAI 2006
80 Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla: Unifying Logical and Statistical AI. AAAI 2006
79EEPedro Domingos: Learning, Logic, and Probability: A Unified View. EKAW 2006: 2
78EEPedro Domingos: Learning, Logic, and Probability: A Unified View. IBERAMIA-SBIA 2006: 3
77EEParag Singla, Pedro Domingos: Entity Resolution with Markov Logic. ICDM 2006: 572-582
76EEPedro Domingos: Learning, Logic, and Probability: A Unified View. PRICAI 2006: 1
75EEMatthew Richardson, Pedro Domingos: Markov logic networks. Machine Learning 62(1-2): 107-136 (2006)
2005
74 Parag Singla, Pedro Domingos: Discriminative Training of Markov Logic Networks. AAAI 2005: 868-873
73 Pedro Domingos, Fernando M. Silva, Horácio C. Neto: An Efficient and Scalable Architecture for Neural Networks with Backpropagation Learning. FPL 2005: 89-94
72EEStanley Kok, Pedro Domingos: Learning the structure of Markov logic networks. ICML 2005: 441-448
71EEDaniel Lowd, Pedro Domingos: Naive Bayes models for probability estimation. ICML 2005: 529-536
70EEParag Singla, Pedro Domingos: Collective Object Identification. IJCAI 2005: 1636-1637
69EEParag Singla, Pedro Domingos: Object Identification with Attribute-Mediated Dependences. PKDD 2005: 297-308
68 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 Popp, Daniel Shapiro, Nathan Schurr, Push Singh, John Yen: Reports on the 2005 AAAI Spring Symposium Series. AI Magazine 26(2): 87-92 (2005)
67EESteffen 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)
2004
66EEPedro Domingos: Learning, Logic, and Probability: A Unified View. ALT 2004: 53
65EEPedro Domingos: Real-World Learning with Markov Logic Networks. ECML 2004: 17
64EEDaniel Grossman, Pedro Domingos: Learning Bayesian network classifiers by maximizing conditional likelihood. ICML 2004
63EEPedro Domingos: Learning, Logic, and Probability: A Unified View. ILP 2004: 359
62EENilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. Sanghai, Deepak Verma: Adversarial classification. KDD 2004: 99-108
61EEPedro Domingos: Real-World Learning with Markov Logic Networks. PKDD 2004: 17
60EERobin Dhamankar, Yoonkyong Lee, AnHai Doan, Alon Y. Halevy, Pedro Domingos: iMAP: Discovering Complex Mappings between Database Schemas. SIGMOD Conference 2004: 383-394
59 AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Y. Halevy: Ontology Matching: A Machine Learning Approach. Handbook on Ontologies 2004: 385-404
58 Matthew Richardson, Pedro Domingos: Combining Link and Content Information in Web Search. Web Dynamics 2004: 179-194
2003
57 Lise Getoor, Ted E. Senator, Pedro 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
56EEPedro Domingos, Matthew Richardson: Learning from Networks of Examples. EPIA 2003: 5
55 Matthew Richardson, Pedro Domingos: Learning with Knowledge from Multiple Experts. ICML 2003: 624-631
54 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
53EEMatthew Richardson, Rakesh Agrawal, Pedro Domingos: Trust Management for the Semantic Web. International Semantic Web Conference 2003: 351-368
52EEMatthew Richardson, Pedro Domingos: Building large knowledge bases by mass collaboration. K-CAP 2003: 129-137
51EETessa A. Lau, Pedro Domingos, Daniel S. Weld: Learning programs from traces using version space algebra. K-CAP 2003: 36-43
50 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)
49EEFoster J. Provost, Pedro Domingos: Tree Induction for Probability-Based Ranking. Machine Learning 52(3): 199-215 (2003)
48EETessa 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)
47EEPedro Domingos: Prospects and challenges for multi-relational data mining. SIGKDD Explorations 5(1): 80-83 (2003)
46EEAnHai 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)
2002
45 Jayant Madhavan, Philip A. Bernstein, Pedro Domingos, Alon Y. Halevy: Representing and Reasoning about Mappings between Domain Models. AAAI/IAAI 2002: 80-86
44EECorin R. Anderson, Pedro Domingos, Daniel S. Weld: Relational Markov models and their application to adaptive web navigation. KDD 2002: 143-152
43EEGeoff Hulten, Pedro Domingos: Mining complex models from arbitrarily large databases in constant time. KDD 2002: 525-531
42EEMatthew Richardson, Pedro Domingos: Mining knowledge-sharing sites for viral marketing. KDD 2002: 61-70
41EEAnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Y. Halevy: Learning to map between ontologies on the semantic web. WWW 2002: 662-673
40EEPedro Domingos: When and How to Subsample: Report on the KDD-2001 Panel. SIGKDD Explorations 3(2): 74-75 (2002)
2001
39EEPedro Domingos, Geoff Hulten: Catching up with the Data: Research Issues in Mining Data Streams. DMKD 2001
38 Pedro Domingos, Geoff Hulten: A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering. ICML 2001: 106-113
37 Corin R. Anderson, Pedro Domingos, Daniel S. Weld: Adaptive Web Navigation for Wireless Devices. IJCAI 2001: 879-884
36EESteven A. Wolfman, Tessa A. Lau, Pedro Domingos, Daniel S. Weld: Mixed initiative interfaces for learning tasks: SMARTedit talks back. IUI 2001: 167-174
35EEPedro Domingos, Matthew Richardson: Mining the network value of customers. KDD 2001: 57-66
34EEGeoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. KDD 2001: 97-106
33EEMatthew Richardson, Pedro Domingos: The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank. NIPS 2001: 1441-1448
32EEPedro Domingos, Geoff Hulten: Learning from Infinite Data in Finite Time. NIPS 2001: 673-680
31EEAnHai Doan, Pedro Domingos, Alon Y. Halevy: Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach. SIGMOD Conference 2001: 509-520
30EECorin R. Anderson, Pedro Domingos, Daniel S. Weld: Personalizing Web Sites for Mobile Users. WWW 2001: 565-575
2000
29 Pedro Domingos: A Unified Bias-Variance Decomposition for Zero-One and Squared Loss. AAAI/IAAI 2000: 564-569
28EEPedro Domingos: Beyond Occam's Razor: Process-Oriented Evaluation. ECML 2000: 3
27 Pedro Domingos: Bayesian Averaging of Classifiers and the Overfitting Problem. ICML 2000: 223-230
26 Pedro Domingos: A Unifeid Bias-Variance Decomposition and its Applications. ICML 2000: 231-238
25 Tessa A. Lau, Pedro Domingos, Daniel S. Weld: Version Space Algebra and its Application to Programming by Demonstration. ICML 2000: 527-534
24EEPedro Domingos, Geoff Hulten: Mining high-speed data streams. KDD 2000: 71-80
23EEAnHai Doan, Pedro Domingos, Alon Y. Levy: Learning Source Description for Data Integration. WebDB (Informal Proceedings) 2000: 81-86
1999
22 Pedro Domingos: Process-Oriented Estimation of Generalization Error. IJCAI 1999: 714-721
21EEPedro Domingos: MetaCost: A General Method for Making Classifiers Cost-Sensitive. KDD 1999: 155-164
20 Pedro Domingos: The Role of Occam's Razor in Knowledge Discovery. Data Min. Knowl. Discov. 3(4): 409-425 (1999)
1998
19 Pedro Domingos: A Process-Oriented Heuristic for Model Selection. ICML 1998: 127-135
18 Pedro Domingos: Occam's Two Razors: The Sharp and the Blunt. KDD 1998: 37-43
17EEPedro Domingos: Knowledge Discovery Via Multiple Models. Intell. Data Anal. 2(1-4): 187-202 (1998)
1997
16 Pedro Domingos: A Comparison of Model Averaging Methods in Foreign Exchange Prediction. AAAI/IAAI 1997: 828
15 Pedro Domingos: Learning Multiple Models without Sacrificing Comprehensibility. AAAI/IAAI 1997: 829
14 Pedro Domingos: Knowledge Acquisition form Examples Vis Multiple Models. ICML 1997: 98-106
13 Pedro Domingos: Why Does Bagging Work? A Bayesian Account and its Implications. KDD 1997: 155-158
12 Pedro Domingos: Control-Sensitive Feature Selection for Lazy Learners. Artif. Intell. Rev. 11(1-5): 227-253 (1997)
11 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)
1996
10 Pedro Domingos: Towards a Unified Approach to Concept Learning. AAAI/IAAI, Vol. 2 1996: 1361
9 Pedro Domingos: Fast Discovery of Simple Rules. AAAI/IAAI, Vol. 2 1996: 1384
8 Pedro Domingos: Multistrategy Learning: A Case Study. AAAI/IAAI, Vol. 2 1996: 1385
7 Pedro Domingos, Michael J. Pazzani: Simple Bayesian Classifiers Do Not Assume Independence. AAAI/IAAI, Vol. 2 1996: 1386
6 Pedro Domingos, Michael J. Pazzani: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. ICML 1996: 105-112
5 Pedro Domingos: Efficient Specific-to-General Rule Induction. KDD 1996: 319-322
4 Pedro Domingos: Linear-Time Rule Induction. KDD 1996: 96-101
3 Pedro Domingos: Unifying Instance-Based and Rule-Based Induction. Machine Learning 24(2): 141-168 (1996)
1995
2 Pedro Domingos: Rule Induction and Instance-Based Learning: A Unified Approach. IJCAI 1995: 1226-1232
1994
1 Pedro Domingos: The RISE System: Conquering without Separating. ICTAI 1994: 704-707

Coauthor Index

1Rakesh Agrawal [53]
2Corin R. Anderson [30] [37] [44] [54]
3Michael L. Anderson [68]
4Thomas Barkowsky [68]
5Philip A. Bernstein [45]
6Pauline M. Berry (Pauline Berry) [68]
7Douglas S. Blank [68]
8Timothy Chklovski [68]
9Nilesh N. Dalvi [62]
10Jesse Davis [101]
11Robin Dhamankar [46] [60]
12Thomas G. Dietterich [89]
13Li Ding [67]
14AnHai Doan [23] [31] [41] [46] [50] [59] [60]
15Marek J. Druzdzel [68]
16Oren Etzioni [54]
17Christos Faloutsos [57]
18Timothy W. Finin (Tim Finin) [67]
19Christian Freksa [68]
20Krzysztof Z. Gajos (Krzysztof Gajos) [54]
21John Gersh [68]
22Lise Getoor [57] [89]
23Jennifer Golbeck [67]
24Daniel Grossman [64]
25Alon Y. Halevy (Alon Y. Levy) [23] [31] [41] [45] [46] [50] [59] [60]
26Mary Hegarty [68]
27Geoff Hulten [24] [32] [34] [38] [39] [43]
28Anupam Joshi [67]
29Stanley Kok [72] [80] [87] [90] [92] [93] [95] [100]
30Tessa A. Lau (Tessa Lau) [25] [36] [48] [51] [54]
31Yoonkyong Lee [60]
32Tze-Yun Leong [68]
33Henry Lieberman [68]
34Daniel Lowd [71] [85] [86] [90] [91] [92] [93]
35Ric K. Lowe [68]
36Susann Luperfoy [68]
37Jayant Madhavan [41] [45] [46] [59]
38 Mausam [62]
39Lisa Meeden [68]
40Rada Mihalcea [68]
41Peter Mika [67]
42David P. Miller [68]
43Stephen Muggleton [89]
44Horácio C. Neto [73]
45Andrzej Nowak [67]
46Tim Oates [68]
47Michael J. Pazzani [6] [7] [11]
48Hoifung Poon [80] [81] [88] [90] [92] [93] [94] [99]
49Robert Popp [68]
50Foster J. Provost [49]
51Matthew Richardson [33] [35] [42] [52] [53] [55] [56] [58] [75] [80] [90] [92] [93]
52Sumit K. Sanghai [62]
53Nathan Schurr [68]
54Ted E. Senator [57]
55Daniel Shapiro [68]
56Fernando M. Silva [73]
57Push Singh [68]
58Parag Singla [69] [70] [74] [77] [80] [82] [84] [90] [92] [93] [98]
59Laurie Spencer [34]
60Steffen Staab [67]
61Marc Sumner [92] [99]
62Prasad Tadepalli [89]
63Robin R. Vallacher [67]
64Deepak Verma [62]
65Jue Wang [92] [97]
66Daniel S. Weld [25] [30] [36] [37] [44] [48] [51] [54]
67Steven A. Wolfman [36] [48] [54]
68John Yen [68]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)