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Pedro Domingos
2010 – today
- 2012
[j19]Pedro Domingos: A few useful things to know about machine learning. Commun. ACM 55(10): 78-87 (2012)- [c106]
- [c105]
- [i7]
- [i6]
- [i5]Hoifung Poon, Pedro Domingos: Sum-Product Networks: A New Deep Architecture. CoRR abs/1202.3732 (2012)
- [i4]
- [i3]
- [i2]
- 2011
- [j18]Jesse Davis, Pedro Domingos: Deep Transfer: A Markov Logic Approach. AI Magazine 32(1): 51-53 (2011)
- [j17]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)
- [c104]Chloe Kiddon, Pedro Domingos: Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models. AAAI 2011
- [c103]Hoifung Poon, Pedro Domingos: Sum-product networks: A new deep architecture. ICCV Workshops 2011: 689-690
- [c102]
- [c101]
- [c100]
- [i1]Pedro Domingos, Sumit K. Sanghai, Daniel S. Weld: Relational Dynamic Bayesian Networks. CoRR abs/1109.2137 (2011)
- 2010
- [c99]Vibhav Gogate, Pedro Domingos: Exploiting Logical Structure in Lifted Probabilistic Inference. Statistical Relational Artificial Intelligence 2010
- [c98]Chloe Kiddon, Pedro Domingos: Leveraging Ontologies for Lifted Probabilistic Inference and Learning. Statistical Relational Artificial Intelligence 2010
- [c97]Stanley Kok, Pedro Domingos: Using Structural Motifs for Learning Markov Logic Networks. Statistical Relational Artificial Intelligence 2010
- [c96]Aniruddh Nath, Pedro Domingos: Efficient Belief Propagation for Utility Maximization and Repeated Inference. AAAI 2010
- [c95]
- [c94]Aniruddh Nath, Pedro Domingos: Efficient Lifting for Online Probabilistic Inference. Statistical Relational Artificial Intelligence 2010
- [c93]Hoifung Poon, Pedro Domingos: Machine Reading: A "Killer App" for Statistical Relational AI. Statistical Relational Artificial Intelligence 2010
- [c92]Parag Singla, Aniruddh Nath, Pedro Domingos: Approximate Lifted Belief Propagation. Statistical Relational Artificial Intelligence 2010
- [c91]
- [c90]
- [c89]Stanley Kok, Pedro Domingos: Learning Markov Logic Networks Using Structural Motifs. ICML 2010: 551-558
- [c88]Vibhav Gogate, William Austin Webb, Pedro Domingos: Learning Efficient Markov Networks. NIPS 2010: 748-756
- [c87]Daniel Lowd, Pedro Domingos: Approximate Inference by Compilation to Arithmetic Circuits. NIPS 2010: 1477-1485
- [c86]
2000 – 2009
- 2009
- [b1]Pedro Domingos, Daniel Lowd: Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2009
- [c85]
- [c84]
- [c83]Stanley Kok, Pedro Domingos: Learning Markov logic network structure via hypergraph lifting. ICML 2009: 64
- 2008
- [j16]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)
- [c82]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
- [c81]
- [c80]
- [c79]Pedro Domingos: Markov logic: a unifying language for knowledge and information management. CIKM 2008: 519
- [c78]Hoifung Poon, Pedro Domingos: Joint Unsupervised Coreference Resolution with Markov Logic. EMNLP 2008: 650-659
- [c77]Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla: Markov Logic. Probabilistic Inductive Logic Programming 2008: 92-117
- [c76]Stanley Kok, Pedro Domingos: Extracting Semantic Networks from Text Via Relational Clustering. ECML/PKDD (1) 2008: 624-639
- [c75]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
- [c74]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
- [c73]
- 2007
- [j15]
- [c72]
- [c71]Pedro Domingos, Parag Singla: Markov Logic in Infinite Domains. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007
- [c70]
- [c69]
- [c68]Daniel Lowd, Pedro Domingos: Efficient Weight Learning for Markov Logic Networks. PKDD 2007: 200-211
- [c67]
- 2006
- [j14]
- [c66]Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla: Unifying Logical and Statistical AI. AAAI 2006: 2-9
- [c65]Hoifung Poon, Pedro Domingos: Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. AAAI 2006: 458-463
- [c64]
- [c63]
- [c62]
- [c61]
- [c60]
- 2005
- [j13]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)
- [j12]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)
- [c59]
- [c58]Timothy Chklovski, Pedro Domingos, Henry Lieberman, Rada Mihalcea, Push Singh: Organizing Committee. AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors 2005
- [c57]Pedro Domingos, Fernando M. Silva, Horácio C. Neto: An Efficient and Scalable Architecture for Neural Networks with Backpropagation Learning. FPL 2005: 89-94
- [c56]
- [c55]
- [c54]
- [c53]Parag Singla, Pedro Domingos: Object Identification with Attribute-Mediated Dependences. PKDD 2005: 297-308
- 2004
- [p2]AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Y. Halevy: Ontology Matching: A Machine Learning Approach. Handbook on Ontologies 2004: 385-404
- [p1]Matthew Richardson, Pedro Domingos: Combining Link and Content Information in Web Search. Web Dynamics 2004: 179-194
- [c52]
- [c51]
- [c50]Daniel Grossman, Pedro Domingos: Learning Bayesian network classifiers by maximizing conditional likelihood. ICML 2004
- [c49]
- [c48]Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. Sanghai, Deepak Verma: Adversarial classification. KDD 2004: 99-108
- [c47]
- [c46]Robin Dhamankar, Yoonkyong Lee, AnHai Doan, Alon Y. Halevy, Pedro Domingos: iMAP: Discovering Complex Mappings between Database Schemas. SIGMOD Conference 2004: 383-394
- 2003
- [j11]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)
- [j10]Foster J. Provost, Pedro Domingos: Tree Induction for Probability-Based Ranking. Machine Learning 52(3): 199-215 (2003)
- [j9]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)
- [j8]Pedro Domingos: Prospects and challenges for multi-relational data mining. SIGKDD Explorations 5(1): 80-83 (2003)
- [j7]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)
- [c45]
- [c44]Matthew Richardson, Pedro Domingos: Learning with Knowledge from Multiple Experts. ICML 2003: 624-631
- [c43]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
- [c42]Tessa A. Lau, Pedro Domingos, Daniel S. Weld: Learning programs from traces using version space algebra. K-CAP 2003: 36-43
- [c41]Matthew Richardson, Pedro Domingos: Building large knowledge bases by mass collaboration. K-CAP 2003: 129-137
- [c40]Matthew Richardson, Rakesh Agrawal, Pedro Domingos: Trust Management for the Semantic Web. International Semantic Web Conference 2003: 351-368
- [e1]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
- 2002
- [j6]Pedro Domingos: When and How to Subsample: Report on the KDD-2001 Panel. SIGKDD Explorations 3(2): 74-75 (2002)
- [c39]Jayant Madhavan, Philip A. Bernstein, Pedro Domingos, Alon Y. Halevy: Representing and Reasoning about Mappings between Domain Models. AAAI/IAAI 2002: 80-86
- [c38]Matthew Richardson, Pedro Domingos: Mining knowledge-sharing sites for viral marketing. KDD 2002: 61-70
- [c37]Corin R. Anderson, Pedro Domingos, Daniel S. Weld: Relational Markov models and their application to adaptive web navigation. KDD 2002: 143-152
- [c36]Geoff Hulten, Pedro Domingos: Mining complex models from arbitrarily large databases in constant time. KDD 2002: 525-531
- [c35]AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Y. Halevy: Learning to map between ontologies on the semantic web. WWW 2002: 662-673
- 2001
- [c34]Pedro Domingos, Geoff Hulten: Catching up with the Data: Research Issues in Mining Data Streams. DMKD 2001
- [c33]Pedro Domingos, Geoff Hulten: A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering. ICML 2001: 106-113
- [c32]Corin R. Anderson, Pedro Domingos, Daniel S. Weld: Adaptive Web Navigation for Wireless Devices. IJCAI 2001: 879-884
- [c31]Steven A. Wolfman, Tessa A. Lau, Pedro Domingos, Daniel S. Weld: Mixed initiative interfaces for learning tasks: SMARTedit talks back. IUI 2001: 167-174
- [c30]
- [c29]
- [c28]
- [c27]Matthew Richardson, Pedro Domingos: The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank. NIPS 2001: 1441-1448
- [c26]AnHai Doan, Pedro Domingos, Alon Y. Halevy: Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach. SIGMOD Conference 2001: 509-520
- [c25]Corin R. Anderson, Pedro Domingos, Daniel S. Weld: Personalizing Web Sites for Mobile Users. WWW 2001: 565-575
- 2000
- [c24]Pedro Domingos: A Unified Bias-Variance Decomposition for Zero-One and Squared Loss. AAAI/IAAI 2000: 564-569
- [c23]
- [c22]
- [c21]
- [c20]Tessa A. Lau, Pedro Domingos, Daniel S. Weld: Version Space Algebra and its Application to Programming by Demonstration. ICML 2000: 527-534
- [c19]
- [c18]AnHai Doan, Pedro Domingos, Alon Y. Levy: Learning Source Description for Data Integration. WebDB (Informal Proceedings) 2000: 81-86
1990 – 1999
- 1999
- [j5]Pedro Domingos: The Role of Occam's Razor in Knowledge Discovery. Data Min. Knowl. Discov. 3(4): 409-425 (1999)
- [c17]
- [c16]
- 1998
- [j4]
- [c15]
- [c14]
- 1997
- [j3]Pedro Domingos: Control-Sensitive Feature Selection for Lazy Learners. Artif. Intell. Rev. 11(1-5): 227-253 (1997)
- [j2]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)
- [c13]Pedro Domingos: A Comparison of Model Averaging Methods in Foreign Exchange Prediction. AAAI/IAAI 1997: 828
- [c12]
- [c11]
- [c10]
- 1996
- [j1]Pedro Domingos: Unifying Instance-Based and Rule-Based Induction. Machine Learning 24(2): 141-168 (1996)
- [c9]
- [c8]
- [c7]
- [c6]Pedro Domingos, Michael J. Pazzani: Simple Bayesian Classifiers Do Not Assume Independence. AAAI/IAAI, Vol. 2 1996: 1386
- [c5]Pedro Domingos, Michael J. Pazzani: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. ICML 1996: 105-112
- [c4]
- [c3]
- 1995
- [c2]Pedro Domingos: Rule Induction and Instance-Based Learning: A Unified Approach. IJCAI 1995: 1226-1232
- 1994
- [c1]
Coauthor Index
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last updated on 2013-02-25 18:44 CET by the dblp team