Pedro M. Domingos

181 papers A* 61A 12B 10Misc 2Journal 39Unranked 48

Year	Rank	Type	Title / Venue / Authors
2021	—	`ch.`	Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. Neuro-Symbolic Artificial Intelligence 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
2019	J	`jnl`	Unifying logical and statistical AI with Markov logic. Commun. ACM Pedro M. Domingos, Daniel Lowd
2018	—	`conf`	Deep Learning as a Mixed Convex-Combinatorial Optimization Problem. ICLR (Poster) Abram L. Friesen, Pedro M. Domingos
2018	—	`conf`	Machine Learning for Data Management: Problems and Solutions. SIGMOD Conference Pedro M. Domingos
2018	A*	`conf`	Submodular Field Grammars: Representation, Inference, and Application to Image Parsing. NeurIPS Abram L. Friesen, Pedro M. Domingos
2017	—	`conf`	Compositional Kernel Machines. ICLR (Workshop) Robert Gens, Pedro M. Domingos
2017	J	`jnl`	Deep Learning as a Mixed Convex-Combinatorial Optimization Problem. CoRR Abram L. Friesen, Pedro M. Domingos
2017	J	`jnl`	Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. CoRR 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
2017	J	`jnl`	On the Latent Variable Interpretation in Sum-Product Networks. IEEE Trans. Pattern Anal. Mach. Intell. Robert Peharz, Robert Gens, Franz Pernkopf, Pedro M. Domingos
2016	A*	`conf`	Learning Tractable Probabilistic Models for Fault Localization. AAAI Aniruddh Nath, Pedro M. Domingos
2016	—	`ch.`	Mining Decision Trees from Streams. Data Stream Management Geoff Hulten, Pedro M. Domingos
2016	J	`jnl`	On the Latent Variable Interpretation in Sum-Product Networks. CoRR Robert Peharz, Robert Gens, Franz Pernkopf, Pedro M. Domingos
2016	J	`jnl`	Pedro Domingos on Big Data Vasant Dhar, Pedro M. Domingos
2016	J	`jnl`	Probabilistic theorem proving. Commun. ACM Vibhav Gogate, Pedro M. Domingos
2016	J	`jnl`	Recursive Decomposition for Nonconvex Optimization. CoRR Abram L. Friesen, Pedro M. Domingos
2016	A*	`conf`	The Sum-Product Theorem: A Foundation for Learning Tractable Models. ICML Abram L. Friesen, Pedro M. Domingos
2016	J	`jnl`	The Sum-Product Theorem: A Foundation for Learning Tractable Models. CoRR Abram L. Friesen, Pedro M. Domingos
2016	A*	`conf`	Unifying Logical and Statistical AI. LICS Pedro M. Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson, Parag Singla
2015	A*	`conf`	Learning Relational Sum-Product Networks. AAAI Aniruddh Nath, Pedro M. Domingos
2015	J	`jnl`	Learning Tractable Probabilistic Models for Fault Localization. CoRR Aniruddh Nath, Pedro M. Domingos
2015	A	`conf`	Learning and Inference in Tractable Probabilistic Knowledge Bases. UAI Mathias Niepert, Pedro M. Domingos
2015	A	`conf`	On Theoretical Properties of Sum-Product Networks. AISTATS Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro M. Domingos
2015	A*	`conf`	Recursive Decomposition for Nonconvex Optimization - IJCAI-15 Distinguished Paper. IJCAI Abram L. Friesen, Pedro M. Domingos
2014	A*	`conf`	Approximate Lifting Techniques for Belief Propagation. AAAI Parag Singla, Aniruddh Nath, Pedro M. Domingos
2014	—	`conf`	Automated Debugging with Tractable Probabilistic Programming. StarAI@AAAI Aniruddh Nath, Pedro M. Domingos
2014	—	`conf`	Deep Symmetry Networks. NIPS Robert Gens, Pedro M. Domingos
2014	A*	`conf`	Exchangeable Variable Models. ICML Mathias Niepert, Pedro M. Domingos
2014	J	`jnl`	Exchangeable Variable Models. CoRR Mathias Niepert, Pedro M. Domingos
2014	—	`conf`	Learning Tractable Statistical Relational Models. StarAI@AAAI Aniruddh Nath, Pedro M. Domingos
2014	—	`conf`	Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond. StarAI@AAAI Mathias Niepert, Pedro M. Domingos
2013	—	`conf`	Learning the Structure of Sum-Product Networks. ICML (3) Robert Gens, Pedro M. Domingos
2013	A	`conf`	Structured Message Passing. UAI Vibhav Gogate, Pedro M. Domingos
2013	J	`jnl`	Structured Message Passing. CoRR Vibhav Gogate, Pedro M. Domingos
2013	—	`conf`	Tractable Probabilistic Knowledge Bases with Existence Uncertainty. StarAI@AAAI William Austin Webb, Pedro M. Domingos
2012	A*	`conf`	A Tractable First-Order Probabilistic Logic. AAAI Pedro M. Domingos, William Austin Webb
2012	J	`jnl`	A few useful things to know about machine learning. Commun. ACM Pedro M. Domingos
2012	J	`jnl`	Approximation by Quantization CoRR Vibhav Gogate, Pedro M. Domingos
2012	—	`conf`	Discriminative Learning of Sum-Product Networks. NIPS Robert Gens, Pedro M. Domingos
2012	J	`jnl`	Formula-Based Probabilistic Inference CoRR Vibhav Gogate, Pedro M. Domingos
2012	—	`conf`	Knowledge Extraction and Joint Inference Using Tractable Markov Logic. AKBC-WEKEX@NAACL-HLT Chloé Kiddon, Pedro M. Domingos
2012	J	`jnl`	Learning Arithmetic Circuits CoRR Daniel Lowd, Pedro M. Domingos
2012	J	`jnl`	Markov Logic in Infinite Domains CoRR Parag Singla, Pedro M. Domingos
2012	J	`jnl`	Probabilistic Theorem Proving CoRR Vibhav Gogate, Pedro M. Domingos
2012	J	`jnl`	Sum-Product Networks: A New Deep Architecture CoRR Hoifung Poon, Pedro M. Domingos
2011	A	`conf`	Approximation by Quantization. UAI Vibhav Gogate, Pedro M. Domingos
2011	A*	`conf`	Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models. AAAI Chloé Kiddon, Pedro M. Domingos
2011	J	`jnl`	Deep Transfer: A Markov Logic Approach. AI Mag. Jesse Davis, Pedro M. Domingos
2011	J	`jnl`	Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning. Mach. Learn. Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro M. Domingos, Kristian Kersting, Xifeng Yan
2011	—	`conf`	Implementing Weighted Abduction in Markov Logic. IWCS James Blythe, Jerry R. Hobbs, Pedro M. Domingos, Rohit J. Kate, Raymond J. Mooney
2011	A	`conf`	Probabilistic Theorem Proving. UAI Vibhav Gogate, Pedro M. Domingos
2011	J	`jnl`	Relational Dynamic Bayesian Networks CoRR Pedro M. Domingos, Sumit K. Sanghai, Daniel S. Weld
2011	A	`conf`	Sum-Product Networks: A New Deep Architecture. UAI Hoifung Poon, Pedro M. Domingos
2011	—	`conf`	Sum-product networks: A new deep architecture. ICCV Workshops Hoifung Poon, Pedro M. Domingos
2010	—	`conf`	Approximate Inference by Compilation to Arithmetic Circuits. NIPS Daniel Lowd, Pedro M. Domingos
2010	—	`conf`	Approximate Lifted Belief Propagation. StarAI@AAAI Parag Singla, Aniruddh Nath, Pedro M. Domingos
2010	A*	`conf`	Bottom-Up Learning of Markov Network Structure. ICML Jesse Davis, Pedro M. Domingos
2010	A*	`conf`	Efficient Belief Propagation for Utility Maximization and Repeated Inference. AAAI Aniruddh Nath, Pedro M. Domingos
2010	A*	`conf`	Efficient Lifting for Online Probabilistic Inference. AAAI Aniruddh Nath, Pedro M. Domingos
2010	—	`conf`	Efficient Lifting for Online Probabilistic Inference. StarAI@AAAI Aniruddh Nath, Pedro M. Domingos
2010	—	`conf`	Exploiting Logical Structure in Lifted Probabilistic Inference. StarAI@AAAI Vibhav Gogate, Pedro M. Domingos
2010	A	`conf`	Formula-Based Probabilistic Inference. UAI Vibhav Gogate, Pedro M. Domingos
2010	—	`conf`	Learning Efficient Markov Networks. NIPS Vibhav Gogate, William Austin Webb, Pedro M. Domingos
2010	A*	`conf`	Learning Markov Logic Networks Using Structural Motifs. ICML Stanley Kok, Pedro M. Domingos
2010	—	`conf`	Leveraging Ontologies for Lifted Probabilistic Inference and Learning. StarAI@AAAI Chloé Kiddon, Pedro M. Domingos
2010	—	`conf`	Machine Reading: A "Killer App" for Statistical Relational AI. StarAI@AAAI Hoifung Poon, Pedro M. Domingos
2010	—	`ch.`	Markov Logic: A Language and Algorithms for Link Mining. Link Mining Pedro M. Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson, Parag Singla
2010	A*	`conf`	Unsupervised Ontology Induction from Text. ACL Hoifung Poon, Pedro M. Domingos
2010	—	`conf`	Using Structural Motifs for Learning Markov Logic Networks. StarAI@AAAI Stanley Kok, Pedro M. Domingos
2009	—	`conf`	A Language for Relational Decision Theory. MLG/SRL@ILP Aniruddh Nath, Pedro M. Domingos
2009	A*	`conf`	Deep transfer via second-order Markov logic. ICML Jesse Davis, Pedro M. Domingos
2009	—	`conf`	Hypergraph Lifting for Structure Learning in Markov Logic Networks. MLG/SRL@ILP Stanley Kok, Pedro M. Domingos
2009	A*	`conf`	Learning Markov logic network structure via hypergraph lifting. ICML Stanley Kok, Pedro M. Domingos
2009	—	`book`	Markov Logic: An Interface Layer for Artificial Intelligence Markov Logic Pedro M. Domingos, Daniel Lowd
2009	—	`ed.`	Proceedings of the Joint 7th Workshop on Mining and Learning with Graphs and the 6th International Workshop on Statistical Relational Learning, MLG/SRL@ILP 2009, Leuven, Belgium, July 2-4, 2009 MLG/SRL@ILP Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro M. Domingos, Kristian Kersting, Xifeng Yan
2009	A*	`conf`	Unsupervised Semantic Parsing. EMNLP Hoifung Poon, Pedro M. Domingos
2008	A*	`conf`	A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC. AAAI Hoifung Poon, Pedro M. Domingos, Marc Sumner
2008	—	`conf`	Extracting Semantic Networks from Text Via Relational Clustering. ECML/PKDD (1) Stanley Kok, Pedro M. Domingos
2008	A*	`conf`	Hybrid Markov Logic Networks. AAAI Jue Wang, Pedro M. Domingos
2008	A*	`conf`	Joint Unsupervised Coreference Resolution with Markov Logic. EMNLP Hoifung Poon, Pedro M. Domingos
2008	—	`conf`	Just Add Weights: Markov Logic for the Semantic Web. URSW (LNCS Vol.) Pedro M. Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla
2008	A	`conf`	Learning Arithmetic Circuits. UAI Daniel Lowd, Pedro M. Domingos
2008	A*	`conf`	Lifted First-Order Belief Propagation. AAAI Parag Singla, Pedro M. Domingos
2008	—	`ch.`	Markov Logic. Probabilistic Inductive Logic Programming Pedro M. Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla
2008	—	`conf`	Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition. SSPR/SPR Pedro M. Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla, Marc Sumner, Jue Wang
2008	A	`conf`	Markov logic: a unifying language for knowledge and information management. CIKM Pedro M. Domingos
2008	J	`jnl`	Structured machine learning: the next ten years. Mach. Learn. Thomas G. Dietterich, Pedro M. Domingos, Lise Getoor, Stephen H. Muggleton, Prasad Tadepalli
2007	—	`conf`	Efficient Weight Learning for Markov Logic Networks. PKDD Daniel Lowd, Pedro M. Domingos
2007	A*	`conf`	Joint Inference in Information Extraction. AAAI Hoifung Poon, Pedro M. Domingos
2007	—	`conf`	Markov Logic in Infinite Domains. Probabilistic, Logical and Relational Learning - A Further Synthesis Pedro M. Domingos, Parag Singla
2007	A	`conf`	Markov Logic in Infinite Domains. UAI Parag Singla, Pedro M. Domingos
2007	A*	`conf`	Recursive Random Fields. IJCAI Daniel Lowd, Pedro M. Domingos
2007	A*	`conf`	Statistical predicate invention. ICML Stanley Kok, Pedro M. Domingos
2007	J	`jnl`	Toward knowledge-rich data mining. Data Min. Knowl. Discov. Pedro M. Domingos
2006	A*	`conf`	Entity Resolution with Markov Logic. ICDM Parag Singla, Pedro M. Domingos
2006	B	`conf`	Learning, Logic, and Probability: A Unified View. EKAW Pedro M. Domingos
2006	—	`conf`	Learning, Logic, and Probability: A Unified View. IBERAMIA-SBIA Pedro M. Domingos
2006	B	`conf`	Learning, Logic, and Probability: A Unified View. PRICAI Pedro M. Domingos
2006	J	`jnl`	Markov logic networks. Mach. Learn. Matthew Richardson, Pedro M. Domingos
2006	A*	`conf`	Memory-Efficient Inference in Relational Domains. AAAI Parag Singla, Pedro M. Domingos
2006	A*	`conf`	Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. AAAI Hoifung Poon, Pedro M. Domingos
2006	A*	`conf`	Unifying Logical and Statistical AI. AAAI Pedro M. Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla
2005	B	`conf`	An Efficient and Scalable Architecture for Neural Networks with Backpropagation Learning. FPL Pedro M. Domingos, Fernando M. Silva, Horácio C. Neto
2005	A*	`conf`	Collective Object Identification. IJCAI Parag Singla, Pedro M. Domingos
2005	A*	`conf`	Discriminative Training of Markov Logic Networks. AAAI Parag Singla, Pedro M. Domingos
2005	A*	`conf`	Learning the structure of Markov logic networks. ICML Stanley Kok, Pedro M. Domingos
2005	A*	`conf`	Naive Bayes models for probability estimation. ICML Daniel Lowd, Pedro M. Domingos
2005	—	`conf`	Object Identification with Attribute-Mediated Dependences. PKDD Parag Singla, Pedro M. Domingos
2005	—	`conf`	Organizing Committee. AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors Timothy Chklovski, Pedro M. Domingos, Henry Lieberman, Rada Mihalcea, Push Singh
2005	J	`jnl`	Reports on the 2005 AAAI Spring Symposium Series. AI Mag. 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
2005	J	`jnl`	Social Networks Applied. IEEE Intell. Syst. Steffen Staab, Pedro M. Domingos, Peter Mika, Jennifer Golbeck, Li Ding, Timothy W. Finin, Anupam Joshi, Andrzej Nowak, Robin R. Vallacher
2004	A*	`conf`	Adversarial classification. KDD Nilesh N. Dalvi, Pedro M. Domingos, Mausam, Sumit K. Sanghai, Deepak Verma
2004	—	`ch.`	Combining Link and Content Information in Web Search. Web Dynamics Matthew Richardson, Pedro M. Domingos
2004	A*	`conf`	Learning Bayesian network classifiers by maximizing conditional likelihood. ICML Daniel Grossman, Pedro M. Domingos
2004	B	`conf`	Learning, Logic, and Probability: A Unified View. ALT Pedro M. Domingos
2004	B	`conf`	Learning, Logic, and Probability: A Unified View. ILP Pedro M. Domingos
2004	—	`ch.`	Ontology Matching: A Machine Learning Approach. Handbook on Ontologies AnHai Doan, Jayant Madhavan, Pedro M. Domingos, Alon Y. Halevy
2004	—	`conf`	Real-World Learning with Markov Logic Networks. ECML Pedro M. Domingos
2004	—	`conf`	Real-World Learning with Markov Logic Networks. PKDD Pedro M. Domingos
2004	—	`conf`	iMAP: Discovering Complex Mappings between Database Schemas. SIGMOD Conference Robin Dhamankar, Yoonkyong Lee, AnHai Doan, Alon Y. Halevy, Pedro M. Domingos
2003	A*	`conf`	Automatically Personalizing User Interfaces. IJCAI Daniel S. Weld, Corin R. Anderson, Pedro M. Domingos, Oren Etzioni, Krzysztof Gajos, Tessa A. Lau, Steven A. Wolfman
2003	B	`conf`	Building large knowledge bases by mass collaboration. K-CAP Matthew Richardson, Pedro M. Domingos
2003	Misc	`conf`	Learning from Networks of Examples. EPIA Pedro M. Domingos, Matthew Richardson
2003	B	`conf`	Learning programs from traces using version space algebra. K-CAP Tessa A. Lau, Pedro M. Domingos, Daniel S. Weld
2003	J	`jnl`	Learning to Match the Schemas of Data Sources: A Multistrategy Approach. Mach. Learn. AnHai Doan, Pedro M. Domingos, Alon Y. Halevy
2003	J	`jnl`	Learning to match ontologies on the Semantic Web. VLDB J. AnHai Doan, Jayant Madhavan, Robin Dhamankar, Pedro M. Domingos, Alon Y. Halevy
2003	A*	`conf`	Learning with Knowledge from Multiple Experts. ICML Matthew Richardson, Pedro M. Domingos
2003	A*	`ed.`	Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, 2003 KDD Lise Getoor, Ted E. Senator, Pedro M. Domingos, Christos Faloutsos
2003	J	`jnl`	Programming by Demonstration Using Version Space Algebra. Mach. Learn. Tessa A. Lau, Steven A. Wolfman, Pedro M. Domingos, Daniel S. Weld
2003	J	`jnl`	Prospects and challenges for multi-relational data mining. SIGKDD Explor. Pedro M. Domingos
2003	J	`jnl`	Tree Induction for Probability-Based Ranking. Mach. Learn. Foster J. Provost, Pedro M. Domingos
2003	Misc	`conf`	Trust Management for the Semantic Web. ISWC Matthew Richardson, Rakesh Agrawal, Pedro M. Domingos
2002	A*	`conf`	Learning to map between ontologies on the semantic web. WWW AnHai Doan, Jayant Madhavan, Pedro M. Domingos, Alon Y. Halevy
2002	A*	`conf`	Mining complex models from arbitrarily large databases in constant time. KDD Geoff Hulten, Pedro M. Domingos
2002	A*	`conf`	Mining knowledge-sharing sites for viral marketing. KDD Matthew Richardson, Pedro M. Domingos
2002	A*	`conf`	Relational Markov models and their application to adaptive web navigation. KDD Corin R. Anderson, Pedro M. Domingos, Daniel S. Weld
2002	—	`conf`	Representing and Reasoning about Mappings between Domain Models. AAAI/IAAI Jayant Madhavan, Philip A. Bernstein, Pedro M. Domingos, Alon Y. Halevy
2002	J	`jnl`	When and How to Subsample: Report on the KDD-2001 Panel. SIGKDD Explor. Pedro M. Domingos
2001	A*	`conf`	A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering. ICML Pedro M. Domingos, Geoff Hulten
2001	A*	`conf`	Adaptive Web Navigation for Wireless Devices. IJCAI Corin R. Anderson, Pedro M. Domingos, Daniel S. Weld
2001	—	`conf`	Catching up with the Data: Research Issues in Mining Data Streams. DMKD Pedro M. Domingos, Geoff Hulten
2001	—	`ch.`	Learning Repetitive Text-Editing Procedures with SMARTedit. Your Wish is My Command Tessa Lau, Steven A. Wolfman, Pedro M. Domingos, Daniel S. Weld
2001	—	`conf`	Learning from Infinite Data in Finite Time. NIPS Pedro M. Domingos, Geoff Hulten
2001	A*	`conf`	Mining the network value of customers. KDD Pedro M. Domingos, Matthew Richardson
2001	A*	`conf`	Mining time-changing data streams. KDD Geoff Hulten, Laurie Spencer, Pedro M. Domingos
2001	A	`conf`	Mixed initiative interfaces for learning tasks: SMARTedit talks back. IUI Steven A. Wolfman, Tessa A. Lau, Pedro M. Domingos, Daniel S. Weld
2001	A*	`conf`	Personalizing Web Sites for Mobile Users. WWW Corin R. Anderson, Pedro M. Domingos, Daniel S. Weld
2001	—	`conf`	Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach. SIGMOD Conference AnHai Doan, Pedro M. Domingos, Alon Y. Halevy
2001	—	`conf`	The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank. NIPS Matthew Richardson, Pedro M. Domingos
2000	A*	`conf`	A Unifeid Bias-Variance Decomposition and its Applications. ICML Pedro M. Domingos
2000	—	`conf`	A Unified Bias-Variance Decomposition for Zero-One and Squared Loss. AAAI/IAAI Pedro M. Domingos
2000	A*	`conf`	Bayesian Averaging of Classifiers and the Overfitting Problem. ICML Pedro M. Domingos
2000	—	`conf`	Beyond Occam's Razor: Process-Oriented Evaluation. ECML Pedro M. Domingos
2000	—	`conf`	Learning Source Description for Data Integration. WebDB (Informal Proceedings) AnHai Doan, Pedro M. Domingos, Alon Y. Levy
2000	A*	`conf`	Mining high-speed data streams. KDD Pedro M. Domingos, Geoff Hulten
2000	A*	`conf`	Version Space Algebra and its Application to Programming by Demonstration. ICML Tessa A. Lau, Pedro M. Domingos, Daniel S. Weld
1999	A*	`conf`	MetaCost: A General Method for Making Classifiers Cost-Sensitive. KDD Pedro M. Domingos
1999	A*	`conf`	Process-Oriented Estimation of Generalization Error. IJCAI Pedro M. Domingos
1999	A	`conf`	Process-oriented evaluation: The next step. AISTATS Pedro M. Domingos
1999	J	`jnl`	The Role of Occam's Razor in Knowledge Discovery. Data Min. Knowl. Discov. Pedro M. Domingos
1998	A*	`conf`	A Process-Oriented Heuristic for Model Selection. ICML Pedro M. Domingos
1998	J	`jnl`	Knowledge Discovery Via Multiple Models. Intell. Data Anal. Pedro M. Domingos
1998	A*	`conf`	Occam's Two Razors: The Sharp and the Blunt. KDD Pedro M. Domingos
1997	—	`conf`	A Comparison of Model Averaging Methods in Foreign Exchange Prediction. AAAI/IAAI Pedro M. Domingos
1997	J	`jnl`	Control-Sensitive Feature Selection for Lazy Learners. Artif. Intell. Rev. Pedro M. Domingos
1997	A*	`conf`	Knowledge Acquisition form Examples Vis Multiple Models. ICML Pedro M. Domingos
1997	—	`conf`	Learning Multiple Models without Sacrificing Comprehensibility. AAAI/IAAI Pedro M. Domingos
1997	J	`jnl`	On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Mach. Learn. Pedro M. Domingos, Michael J. Pazzani
1997	A*	`conf`	Why Does Bagging Work? A Bayesian Account and its Implications. KDD Pedro M. Domingos
1996	A*	`conf`	Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. ICML Pedro M. Domingos, Michael J. Pazzani
1996	A*	`conf`	Efficient Specific-to-General Rule Induction. KDD Pedro M. Domingos
1996	—	`conf`	Fast Discovery of Simple Rules. AAAI/IAAI, Vol. 2 Pedro M. Domingos
1996	A*	`conf`	Linear-Time Rule Induction. KDD Pedro M. Domingos
1996	—	`conf`	Multistrategy Learning: A Case Study. AAAI/IAAI, Vol. 2 Pedro M. Domingos
1996	—	`conf`	Simple Bayesian Classifiers Do Not Assume Independence. AAAI/IAAI, Vol. 2 Pedro M. Domingos, Michael J. Pazzani
1996	—	`conf`	Towards a Unified Approach to Concept Learning. AAAI/IAAI, Vol. 2 Pedro M. Domingos
1996	J	`jnl`	Two-Way Induction. Int. J. Artif. Intell. Tools Pedro M. Domingos
1996	J	`jnl`	Unifying Instance-Based and Rule-Based Induction. Mach. Learn. Pedro M. Domingos
1995	B	`conf`	Progressive rules: a method for representing and using real-time knowledge. ICTAI Pedro M. Domingos, Ernesto M. Morgado
1995	A*	`conf`	Rule Induction and Instance-Based Learning: A Unified Approach. IJCAI Pedro M. Domingos
1995	B	`conf`	Two-way induction. ICTAI Pedro M. Domingos
1994	B	`conf`	The RISE System: Conquering without Separating. ICTAI Pedro M. Domingos