Pedro Domingos

	2012
131		Vibhav Gogate, Pedro Domingos: Approximation by Quantization CoRR abs/1202.3723: (2012)
130		Vibhav Gogate, Pedro Domingos: Probabilistic Theorem Proving CoRR abs/1202.3724: (2012)
129		Hoifung Poon, Pedro Domingos: Sum-Product Networks: A New Deep Architecture CoRR abs/1202.3732: (2012)
128		Vibhav Gogate, Pedro Domingos: Formula-Based Probabilistic Inference CoRR abs/1203.3482: (2012)
	2011
127		Chloe Kiddon, Pedro Domingos: Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models. AAAI 2011
126		Hoifung Poon, Pedro Domingos: Sum-product networks: A new deep architecture. ICCV Workshops 2011: 689-690
125		Vibhav Gogate, Pedro Domingos: Approximation by Quantization. UAI 2011: 247-255
124		Vibhav Gogate, Pedro Domingos: Probabilistic Theorem Proving. UAI 2011: 256-265
123		Hoifung Poon, Pedro Domingos: Sum-Product Networks: A New Deep Architecture. UAI 2011: 337-346
122		Jesse Davis, Pedro Domingos: Deep Transfer: A Markov Logic Approach. AI Magazine 32(1): 51-53 (2011)
121		Pedro Domingos, Sumit K. Sanghai, Daniel S. Weld: Relational Dynamic Bayesian Networks CoRR abs/1109.2137: (2011)
120		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)
	2010
119		Aniruddh Nath, Pedro Domingos: Efficient Belief Propagation for Utility Maximization and Repeated Inference. AAAI 2010
118		Aniruddh Nath, Pedro Domingos: Efficient Lifting for Online Probabilistic Inference. AAAI 2010
117		Hoifung Poon, Pedro Domingos: Unsupervised Ontology Induction from Text. ACL 2010: 296-305
116		Jesse Davis, Pedro Domingos: Bottom-Up Learning of Markov Network Structure. ICML 2010: 271-278
115		Stanley Kok, Pedro Domingos: Learning Markov Logic Networks Using Structural Motifs. ICML 2010: 551-558
114		Daniel Lowd, Pedro Domingos: Approximate Inference by Compilation to Arithmetic Circuits. NIPS 2010: 1477-1485
113		Vibhav Gogate, William Austin Webb, Pedro Domingos: Learning Efficient Markov Networks. NIPS 2010: 748-756
112		Parag Singla, Aniruddh Nath, Pedro Domingos: Approximate Lifted Belief Propagation. Statistical Relational Artificial Intelligence 2010
111		Aniruddh Nath, Pedro Domingos: Efficient Lifting for Online Probabilistic Inference. Statistical Relational Artificial Intelligence 2010
110		Vibhav Gogate, Pedro Domingos: Exploiting Logical Structure in Lifted Probabilistic Inference. Statistical Relational Artificial Intelligence 2010
109		Chloe Kiddon, Pedro Domingos: Leveraging Ontologies for Lifted Probabilistic Inference and Learning. Statistical Relational Artificial Intelligence 2010
108		Hoifung Poon, Pedro Domingos: Machine Reading: A "Killer App" for Statistical Relational AI. Statistical Relational Artificial Intelligence 2010
107		Stanley Kok, Pedro Domingos: Using Structural Motifs for Learning Markov Logic Networks. Statistical Relational Artificial Intelligence 2010
106		Vibhav Gogate, Pedro Domingos: Formula-Based Probabilistic Inference. UAI 2010: 210-219
	2009
105		Pedro Domingos, Daniel Lowd: Markov Logic: An Interface Layer for Artificial Intelligence Morgan & Claypool Publishers 2009
104		Hoifung Poon, Pedro Domingos: Unsupervised Semantic Parsing. EMNLP 2009: 1-10
103		Jesse Davis, Pedro Domingos: Deep transfer via second-order Markov logic. ICML 2009: 28
102		Stanley Kok, Pedro Domingos: Learning Markov logic network structure via hypergraph lifting. ICML 2009: 64
	2008
101		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
100		Parag Singla, Pedro Domingos: Lifted First-Order Belief Propagation. AAAI 2008: 1094-1099
99		Jue Wang, Pedro Domingos: Hybrid Markov Logic Networks. AAAI 2008: 1106-1111
98		Pedro Domingos: Markov logic: a unifying language for knowledge and information management. CIKM 2008: 519
97		Stanley Kok, Pedro Domingos: Extracting Semantic Networks from Text Via Relational Clustering. ECML/PKDD (1) 2008: 624-639
96		Hoifung Poon, Pedro Domingos: Joint Unsupervised Coreference Resolution with Markov Logic. EMNLP 2008: 650-659
95		Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla: Markov Logic. Probabilistic Inductive Logic Programming 2008: 92-117
94		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
93		Daniel Lowd, Pedro Domingos: Learning Arithmetic Circuits. UAI 2008: 383-392
92		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
91		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)
	2007
90		Hoifung Poon, Pedro Domingos: Joint Inference in Information Extraction. AAAI 2007: 913-918
89		Stanley Kok, Pedro Domingos: Statistical predicate invention. ICML 2007: 433-440
88		Daniel Lowd, Pedro Domingos: Recursive Random Fields. IJCAI 2007: 950-955
87		Daniel Lowd, Pedro Domingos: Efficient Weight Learning for Markov Logic Networks. PKDD 2007: 200-211
86		Pedro Domingos, Parag Singla: Markov Logic in Infinite Domains. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007
85		Parag Singla, Pedro Domingos: Markov Logic in Infinite Domains. UAI 2007: 368-375
84		Pedro Domingos: Toward knowledge-rich data mining. Data Min. Knowl. Discov. 15(1): 21-28 (2007)
	2006
83		Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla: Unifying Logical and Statistical AI. AAAI 2006: 2-9
82		Hoifung Poon, Pedro Domingos: Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. AAAI 2006: 458-463
81		Parag Singla, Pedro Domingos: Memory-Efficient Inference in Relational Domains. AAAI 2006: 488-493
80		Pedro Domingos: Learning, Logic, and Probability: A Unified View. EKAW 2006: 2
79		Pedro Domingos: Learning, Logic, and Probability: A Unified View. IBERAMIA-SBIA 2006: 3
78		Parag Singla, Pedro Domingos: Entity Resolution with Markov Logic. ICDM 2006: 572-582
77		Pedro Domingos: Learning, Logic, and Probability: A Unified View. PRICAI 2006: 1
76		Matthew Richardson, Pedro Domingos: Markov logic networks. Machine Learning 62(1-2): 107-136 (2006)
	2005
75		Parag Singla, Pedro Domingos: Discriminative Training of Markov Logic Networks. AAAI 2005: 868-873
74		Timothy Chklovski, Pedro Domingos, Henry Lieberman, Rada Mihalcea, Push Singh: Organizing Committee. AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors 2005
73		Pedro Domingos, Fernando M. Silva, Horácio C. Neto: An Efficient and Scalable Architecture for Neural Networks with Backpropagation Learning. FPL 2005: 89-94
72		Stanley Kok, Pedro Domingos: Learning the structure of Markov logic networks. ICML 2005: 441-448
71		Daniel Lowd, Pedro Domingos: Naive Bayes models for probability estimation. ICML 2005: 529-536
70		Parag Singla, Pedro Domingos: Collective Object Identification. IJCAI 2005: 1636-1637
69		Parag 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 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)
67		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)
	2004
66		Pedro Domingos: Learning, Logic, and Probability: A Unified View. ALT 2004: 53
65		Pedro Domingos: Real-World Learning with Markov Logic Networks. ECML 2004: 17
64		Daniel Grossman, Pedro Domingos: Learning Bayesian network classifiers by maximizing conditional likelihood. ICML 2004
63		Pedro Domingos: Learning, Logic, and Probability: A Unified View. ILP 2004: 359
62		Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. Sanghai, Deepak Verma: Adversarial classification. KDD 2004: 99-108
61		Pedro Domingos: Real-World Learning with Markov Logic Networks. PKDD 2004: 17
60		Robin 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
56		Pedro 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
53		Matthew Richardson, Rakesh Agrawal, Pedro Domingos: Trust Management for the Semantic Web. International Semantic Web Conference 2003: 351-368
52		Matthew Richardson, Pedro Domingos: Building large knowledge bases by mass collaboration. K-CAP 2003: 129-137
51		Tessa 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)
49		Foster J. Provost, Pedro Domingos: Tree Induction for Probability-Based Ranking. Machine Learning 52(3): 199-215 (2003)
48		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)
47		Pedro Domingos: Prospects and challenges for multi-relational data mining. SIGKDD Explorations 5(1): 80-83 (2003)
46		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)
	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
44		Corin R. Anderson, Pedro Domingos, Daniel S. Weld: Relational Markov models and their application to adaptive web navigation. KDD 2002: 143-152
43		Geoff Hulten, Pedro Domingos: Mining complex models from arbitrarily large databases in constant time. KDD 2002: 525-531
42		Matthew Richardson, Pedro Domingos: Mining knowledge-sharing sites for viral marketing. KDD 2002: 61-70
41		AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Y. Halevy: Learning to map between ontologies on the semantic web. WWW 2002: 662-673
40		Pedro Domingos: When and How to Subsample: Report on the KDD-2001 Panel. SIGKDD Explorations 3(2): 74-75 (2002)
	2001
39		Pedro 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
36		Steven A. Wolfman, Tessa A. Lau, Pedro Domingos, Daniel S. Weld: Mixed initiative interfaces for learning tasks: SMARTedit talks back. IUI 2001: 167-174
35		Pedro Domingos, Matthew Richardson: Mining the network value of customers. KDD 2001: 57-66
34		Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. KDD 2001: 97-106
33		Matthew Richardson, Pedro Domingos: The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank. NIPS 2001: 1441-1448
32		Pedro Domingos, Geoff Hulten: Learning from Infinite Data in Finite Time. NIPS 2001: 673-680
31		AnHai Doan, Pedro Domingos, Alon Y. Halevy: Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach. SIGMOD Conference 2001: 509-520
30		Corin 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
28		Pedro 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
24		Pedro Domingos, Geoff Hulten: Mining high-speed data streams. KDD 2000: 71-80
23		AnHai 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
21		Pedro 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
17		Pedro 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

1	Rakesh Agrawal	[53]
2	Corin R. Anderson	[30] [37] [44] [54]
3	Michael L. Anderson	[68]
4	Thomas Barkowsky	[68]
5	Philip A. Bernstein	[45]
6	Pauline M. Berry (Pauline Berry)	[68]
7	Douglas S. Blank	[68]
8	Hendrik Blockeel	[120]
9	Karsten M. Borgwardt	[120]
10	Timothy Chklovski	[68] [74]
11	Nilesh N. Dalvi	[62]
12	Jesse Davis	[103] [116] [122]
13	Robin Dhamankar	[46] [60]
14	Thomas G. Dietterich	[91]
15	Li Ding	[67]
16	AnHai Doan	[23] [31] [41] [46] [50] [59] [60]
17	Marek J. Druzdzel	[68]
18	Oren Etzioni	[54]
19	Christos Faloutsos	[57]
20	Timothy W. Finin (Tim Finin)	[67]
21	Christian Freksa	[68]
22	Krzysztof Z. Gajos (Krzysztof Gajos)	[54]
23	John Gersh	[68]
24	Lise Getoor	[57] [91]
25	Vibhav Gogate	[106] [110] [113] [124] [125] [128] [130] [131]
26	Jennifer Golbeck	[67]
27	Daniel Grossman	[64]
28	Alon Y. Halevy (Alon Y. Levy)	[23] [31] [41] [45] [46] [50] [59] [60]
29	Mary Hegarty	[68]
30	Geoff Hulten	[24] [32] [34] [38] [39] [43]
31	Anupam Joshi	[67]
32	Kristian Kersting	[120]
33	Chloe Kiddon	[109] [127]
34	Stanley Kok	[72] [83] [89] [92] [94] [95] [97] [102] [107] [115]
35	Tessa A. Lau (Tessa Lau)	[25] [36] [48] [51] [54]
36	Yoonkyong Lee	[60]
37	Tze-Yun Leong	[68]
38	Henry Lieberman	[68] [74]
39	Daniel Lowd	[71] [87] [88] [92] [93] [94] [95] [105] [114]
40	Ric K. Lowe	[68]
41	Susann Luperfoy	[68]
42	Jayant Madhavan	[41] [45] [46] [59]
43	Mausam	[62]
44	Lisa Meeden	[68]
45	Rada Mihalcea	[68] [74]
46	Peter Mika	[67]
47	David P. Miller	[68]
48	Stephen Muggleton	[91]
49	Aniruddh Nath	[111] [112] [118] [119]
50	Horácio C. Neto	[73]
51	Andrzej Nowak	[67]
52	Tim Oates	[68]
53	Michael J. Pazzani	[6] [7] [11]
54	Hoifung Poon	[82] [83] [90] [92] [94] [95] [96] [101] [104] [108] [117] [123] [126] [129]
55	Robert L. Popp	[68]
56	Foster J. Provost	[49]
57	Luc De Raedt	[120]
58	Matthew Richardson	[33] [35] [42] [52] [53] [55] [56] [58] [76] [83] [92] [94] [95]
59	Sumit K. Sanghai	[62] [121]
60	Nathan Schurr	[68]
61	Ted E. Senator	[57]
62	Daniel G. Shapiro	[68]
63	Fernando M. Silva	[73]
64	Push Singh	[68] [74]
65	Parag Singla	[69] [70] [75] [78] [81] [83] [85] [86] [92] [94] [95] [100] [112]
66	Laurie Spencer	[34]
67	Steffen Staab	[67]
68	Marc Sumner	[94] [101]
69	Prasad Tadepalli	[91]
70	Robin R. Vallacher	[67]
71	Deepak Verma	[62]
72	Jue Wang	[94] [99]
73	William Austin Webb	[113]
74	Daniel S. Weld	[25] [30] [36] [37] [44] [48] [51] [54] [121]
75	Steven A. Wolfman	[36] [48] [54]
76	Xifeng Yan	[120]
77	John Yen	[68]

Colors in the list of coauthors

Last update Tue May 29 20:41:18 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page