- Adaptive Web Navigation for Wireless Devices Corin R. Anderson, Pedro Domingos, Daniel S. Weld
- Adversarial Classification Nilesh Dalvi Pedro Domingos Mausam Sumit Sanghai Deepak Verma
- Hybrid Markov Logic Networks Jue Wang Pedro Domingos
- Mining Complex Models from Arbitrarily Large Databases in Constant Time
- The VLDB Journal manuscript No. (will be inserted by the editor)
- Efficient Belief Propagation for Utility Maximization and Repeated Inference Aniruddh Nath and Pedro Domingos
- Joint Inference in Information Extraction Hoifung Poon Pedro Domingos
- Bottom-Up Learning of Markov Network Structure Jesse Davis jdavis@cs.washington.edu
- Recursive Random Fields Daniel Lowd and Pedro Domingos
- Markov Logic Networks Matthew Richardson (mattr@cs.washington.edu) and
- Markov Logic Pedro Domingos1
- Learning Programs from Traces using Version Space Algebra
- PEDRO M. DOMINGOS Department of Computer Science and Engineering Tel. (206) 543-4229
- Programming by demonstration using version space algebra
- Unifying Logical and Statistical AI Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla
- Trust Management for the Semantic Web Matthew Richardson1
- Relational Markov Models and their Application to Adaptive Web Navigation
- 1 Markov Logic: A Unifying Framework for Statistical Relational Learning
- Deep Transfer via Second-Order Markov Logic Jesse Davis jdavis@cs.washington.edu
- E4 --Machine Learning Pedro Domingos
- Learning Markov Logic Networks Using Structural Motifs Stanley Kok koks@cs.washington.edu
- Probabilistic Theorem Proving Vibhav Gogate and Pedro Domingos
- Mining Massive Relational Databases Geoff Hulten, Pedro Domingos, and Yeuhi Abe
- Multi-Relational Record Linkage Parag and Pedro Domingos
- Extracting Semantic Networks from Text Via Relational Clustering
- Building Large Knowledge Bases by Mass Collaboration Matthew Richardson
- A Unified Bias-Variance Decomposition and its Applications Pedro Domingos pedrod@cs.washington.edu
- Statistical Predicate Invention Stanley Kok koks@cs.washington.edu
- Markov Logic in Infinite Domains Parag Singla Pedro Domingos
- , , 119 () c Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- A General Method for Reducing the Complexity of Relational Inference And its Application to MCMC
- Efficient Weight Learning for Markov Logic Networks
- A General Framework for Mining Massive Data Streams
- A Language for Relational Decision Theory Aniruddh Nath nath@cs.washington.edu
- Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
- Learning Markov Logic Network Structure via Hypergraph Lifting Stanley Kok koks@cs.washington.edu
- Learning Efficient Markov Networks Vibhav Gogate William Austin Webb Pedro Domingos
- Object Identification with Attribute-Mediated Dependences
- Naive Bayes Models for Probability Estimation Daniel Lowd LOWD@CS.WASHINGTON.EDU
- Mining High-Speed Data Streams Pedro Domingos
- Memory-Efficient Inference in Relational Domains Parag Singla Pedro Domingos
- Learning Arithmetic Circuits Daniel Lowd and Pedro Domingos
- The Intelligent Surfer: Probabilistic Combination of Link and
- Sum-Product Networks: A New Deep Architecture Hoifung Poon and Pedro Domingos
- What's Missing in AI: The Interface Layer Pedro Domingos
- Solving AI: We Need a New Language for Artificial Intelligence
- Structured Machine Learning: Ten Problems for the Next Ten Years
- Toward Knowledge-Rich Data Mining Pedro Domingos
- Mining Social Networks for Viral Marketing Pedro Domingos
- Approximation by Quantization Vibhav Gogate and Pedro Domingos
- Approximate Inference by Compilation to Arithmetic Circuits
- Formula-Based Probabilistic Inference Vibhav Gogate and Pedro Domingos
- Unsupervised Ontology Induction from Text Hoifung Poon and Pedro Domingos
- Efficient Lifting for Online Probabilistic Inference Aniruddh Nath and Pedro Domingos
- Unsupervised Semantic Parsing Hoifung Poon Pedro Domingos
- Joint Unsupervised Coreference Resolution with Markov Logic Hoifung Poon Pedro Domingos
- Lifted First-Order Belief Propagation Parag Singla Pedro Domingos
- Entity Resolution with Markov Logic Parag Singla Pedro Domingos
- Sound and Efficient Inference with Probabilistic and Deterministic Dependencies Hoifung Poon Pedro Domingos
- Learning the Structure of Markov Logic Networks Stanley Kok KOKS@CS.WASHINGTON.EDU
- Discriminative Training of Markov Logic Networks Parag Singla Pedro Domingos
- Markov Logic: A Unifying Framework for Statistical Relational Learning
- Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood
- Mining Knowledge-Sharing Sites for Viral Marketing Matthew Richardson and Pedro Domingos
- Representing and Reasoning about Mappings between Domain Models Jayant Madhavan
- A Unified Bias-Variance Decomposition for Zero-One and Squared Loss Pedro Domingos
- Version Space Algebra and its Application to Programming by Demonstration
- Bayesian Averaging of Classifiers and the Overfitting Problem Pedro Domingos pedrod@cs.washington.edu
- Learning Source Descriptions for Data Integration AnHai Doan, Pedro Domingos, Alon Levy
- Process-Oriented Estimation of Generalization Error Pedro Domingos
- The Alchemy Tutorial Marc Sumner Pedro Domingos
