#2,709

Most Influential Person

Professor of machine learning

According to Wikipedia, Pedro Domingos is a Professor Emeritus of computer science and engineering at the University of Washington. He is a researcher in machine learning known for Markov logic network enabling uncertain inference.

- On the Optimality of the Simple Bayesian Classifier under Zero-One Loss (1997) (3162)
- Markov logic networks (2006) (2839)
- Mining the network value of customers (2001) (2717)
- A few useful things to know about machine learning (2012) (2272)
- Mining high-speed data streams (2000) (2134)
- Mining time-changing data streams (2001) (1711)
- Mining knowledge-sharing sites for viral marketing (2002) (1699)
- MetaCost: a general method for making classifiers cost-sensitive (1999) (1515)
- Learning to map between ontologies on the semantic web (2002) (1073)
- Reconciling schemas of disparate data sources: a machine-learning approach (2001) (897)
- Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier (1996) (877)
- Adversarial classification (2004) (876)
- Trust Management for the Semantic Web (2003) (613)
- Sum-product networks: A new deep architecture (2011) (603)
- Learning to match ontologies on the Semantic Web (2003) (581)
- Tree Induction for Probability-Based Ranking (2003) (576)
- Ontology Matching: A Machine Learning Approach (2004) (559)
- Markov Logic: An Interface Layer for Artificial Intelligence (2009) (455)
- The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank (2001) (441)
- iMAP: discovering complex semantic matches between database schemas (2004) (426)
- Entity Resolution with Markov Logic (2006) (409)
- The Role of Occam's Razor in Knowledge Discovery (1999) (399)
- Learning the structure of Markov logic networks (2005) (341)
- Social Networks Applied (2005) (336)
- Learning Bayesian network classifiers by maximizing conditional likelihood (2004) (314)
- The Alchemy System for Statistical Relational AI: User Manual (2007) (312)
- Naive Bayes models for probability estimation (2005) (300)
- Sound and Efficient Inference with Probabilistic and Deterministic Dependencies (2006) (298)
- Lifted First-Order Belief Propagation (2008) (295)
- Discriminative Training of Markov Logic Networks (2005) (293)
- Joint Inference in Information Extraction (2007) (290)
- Learning to Match the Schemas of Data Sources: A Multistrategy Approach (2003) (284)
- Representing and reasoning about mappings between domain models (2002) (280)
- Joint Unsupervised Coreference Resolution with Markov Logic (2008) (243)
- Efficient Weight Learning for Markov Logic Networks (2007) (241)
- Mining Social Networks for Viral Marketing (232)
- The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (2015) (232)
- Deep Symmetry Networks (2014) (230)
- Deep transfer via second-order Markov logic (2009) (221)
- Learning the Structure of Sum-Product Networks (2013) (220)
- A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering (2001) (218)
- Statistical predicate invention (2007) (216)
- Relational Markov models and their application to adaptive web navigation (2002) (195)
- Programming by Demonstration Using Version Space Algebra (2003) (193)
- Probabilistic theorem proving (2011) (190)
- Discriminative Learning of Sum-Product Networks (2012) (184)
- Unifying instance-based and rule-based induction (2004) (182)
- Multi-Relational Record Linkage (2003) (182)
- Bayesian Averaging of Classifiers and the Overfitting Problem (2000) (173)
- Catching up with the Data: Research Issues in Mining Data Streams (2001) (171)
- A General Framework for Mining Massive Data Streams (2003) (163)
- A Unified Bias-Variance Decomposition for Zero-One and Squared Loss (2000) (162)
- A Unified Bias-Variance Decomposition and its Applications (2000) (161)
- A Unifeid Bias-Variance Decomposition and its Applications (2000) (154)
- Personalizing web sites for mobile users (2001) (149)
- Hybrid Markov Logic Networks (2008) (148)
- Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017) (147)
- Adaptive Web Navigation for Wireless Devices (2001) (145)
- Unsupervised Ontology Induction from Text (2010) (142)
- Structured machine learning: the next ten years (2008) (131)
- Knowledge Discovery Via Multiple Models (1998) (129)
- Learning Markov logic network structure via hypergraph lifting (2009) (129)
- Automatically Personalizing User Interfaces (2003) (128)
- Rule Induction and Instance-Based Learning: A Unified Approach (1995) (127)
- Learning Markov Logic Networks Using Structural Motifs (2010) (125)
- Extracting Semantic Networks from Text Via Relational Clustering (2008) (123)
- iMAP: Discovering Complex Mappings between Database Schemas. (2004) (121)
- 1 Markov Logic: A Unifying Framework for Statistical Relational Learning (2010) (119)
- Why Does Bagging Work? A Bayesian Account and its Implications (1997) (117)
- Memory-Efficient Inference in Relational Domains (2006) (115)
- Version Space Algebra and its Application to Programming by Demonstration (2000) (110)
- Learning Source Description for Data Integration (2000) (106)
- Knowledge Acquisition from Examples Via Multiple Models (1997) (106)
- Learning Arithmetic Circuits (2008) (105)
- Occam's Two Razors: The Sharp and the Blunt (1998) (101)
- Building large knowledge bases by mass collaboration (2003) (100)
- Learning Source Descriptions for Data Integration (2000) (99)
- Markov Logic (2008) (98)
- Learning with Knowledge from Multiple Experts (2003) (97)
- Control-Sensitive Feature Selection for Lazy Learners (1997) (94)
- A Unified Bias-Variance Decomposition (92)
- On the Latent Variable Interpretation in Sum-Product Networks (2016) (90)
- A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC (2008) (90)
- Prospects and challenges for multi-relational data mining (2003) (87)
- Unifying Instance-Based and Rule-Based Induction (1996) (87)
- Markov Logic in Infinite Domains (2007) (84)
- Learning to map between structured representations of data (2002) (83)
- Object Identification with Attribute-Mediated Dependences (2005) (82)
- Dynamic Probabilistic Relational Models (2003) (77)
- On Theoretical Properties of Sum-Product Networks (2015) (76)
- Mining complex models from arbitrarily large databases in constant time (2002) (73)
- KDD-2003 : proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC, USA (2003) (64)
- Toward knowledge-rich data mining (2007) (62)
- A Tractable First-Order Probabilistic Logic (2012) (61)
- Learning programs from traces using version space algebra (2003) (60)
- Unifying Logical and Statistical AI (2006) (56)
- Learning Selective Sum-Product Networks (2014) (50)
- Bottom-Up Learning of Markov Network Structure (2010) (50)
- Programming by demonstration: a machine learning approach (2001) (49)
- Relational Dynamic Bayesian Networks (2005) (47)
- Mixed initiative interfaces for learning tasks: SMARTedit talks back (2001) (43)
- Learning Efficient Markov Networks (2010) (43)
- Knowledge Acquisition form Examples Vis Multiple Models (1997) (42)
- Learning Repetitive Text-Editing Procedures with SMARTedit (2001) (41)
- A machine learning approach to web personalization (2002) (40)
- Machine Reading at the University of Washington (2010) (38)
- Just Add Weights: Markov Logic for the Semantic Web (2008) (38)
- How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications (1998) (37)
- Using Partitioning to Speed Up Specific-to-General Rule Induction (1996) (36)
- The RISE system: conquering without separating (1994) (36)
- A Language for Relational Decision Theory (2009) (34)
- Implementing Weighted Abduction in Markov Logic (2011) (34)
- Learning from Infinite Data in Finite Time (2001) (34)
- Efficient Belief Propagation for Utility Maximization and Repeated Inference (2010) (33)
- Recursive Decomposition for Nonconvex Optimization (2015) (33)
- Approximate Lifting Techniques for Belief Propagation (2014) (32)
- Linear-Time Rule Induction (1996) (31)
- The Sum-Product Theorem: A Foundation for Learning Tractable Models (2016) (30)
- Every Model Learned by Gradient Descent Is Approximately a Kernel Machine (2020) (30)
- Efficient Lifting for Online Probabilistic Inference (2010) (30)
- Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, 2003 (2003) (29)
- A Process-Oriented Heuristic for Model Selection (1998) (29)
- Exploiting Logical Structure in Lifted Probabilistic Inference (2010) (29)
- Learning Relational Sum-Product Networks (2015) (28)
- Formula-Based Probabilistic Inference (2010) (26)
- Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models (2011) (26)
- Learning Tractable Probabilistic Models for Fault Localization (2015) (23)
- Approximate Lifted Belief Propagation (2010) (22)
- Machine Reading: A "Killer App" for Statistical Relational AI (2010) (22)
- Recursive Random Fields (2007) (22)
- Mining massive data streams (2005) (20)
- Mining Massive Relational Databases (2003) (20)
- Efficient Specific-to-General Rule Induction (1996) (19)
- Structured Message Passing (2013) (19)
- What ’ s Missing in AI : The Interface Layer (18)
- Unifying logical and statistical AI with Markov logic (2019) (17)
- Approximate Inference by Compilation to Arithmetic Circuits (2010) (16)
- Process-Oriented Estimation of Generalization Error (1999) (16)
- Markov logic: a unifying language for knowledge and information management (2008) (16)
- An efficient and scalable architecture for neural networks with backpropagation learning (2005) (16)
- Web Site Personalizers for Mobile Devices (2001) (16)
- Approximation by Quantization (2011) (15)
- Deep Learning as a Mixed Convex-Combinatorial Optimization Problem (2017) (15)
- The RISE 2.0 System: A Case Study in Multistrategy Learning (1995) (15)
- Exchangeable Variable Models (2014) (13)
- When and how to subsample: report on the KDD-2001 panel (2002) (12)
- “It’s Going to Kill Us!” and Other Myths About the Future of Artificial Intelligence (2016) (11)
- Two-way induction (1995) (11)
- Deep Transfer: A Markov Logic Approach (2011) (11)
- Tractable Probabilistic Knowledge Bases with Existence Uncertainty (2013) (10)
- Learning Mappings between Data Schemas (2000) (10)
- Machine Learning for Data Management: Problems and Solutions (2018) (9)
- Learning Models of Relational Stochastic Processes (2005) (9)
- Learning and inference in collective knowledge bases (2004) (9)
- Towards a Unified Approach to Concept Learning (1996) (9)
- Real-World Learning with Markov Logic Networks (2004) (8)
- Learning and Inference in Tractable Probabilistic Knowledge Bases (2015) (8)
- Combining Link and Content Information in Web Search (2004) (7)
- Symmetry-Based Semantic Parsing (2015) (7)
- Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity (2020) (7)
- Markov Logic: A Language and Algorithms for Link Mining (2010) (6)
- Compositional Kernel Machines (2017) (6)
- Structure learning in markov logic networks (2010) (6)
- Learning Multiple Models without Sacrificing Comprehensibility (1997) (6)
- Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond (2014) (6)
- Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition (2008) (5)
- Learning Tractable Statistical Relational Models (2014) (5)
- Bayesian Model Averaging in Rule Induction (1997) (5)
- Building the Semantic Web by Mass Collaboration (2003) (5)
- Collective Object Identification (2005) (5)
- Markov logic: theory, algorithms and applications (2009) (4)
- Mixed initiative interfaces for learning tasks (2001) (4)
- Leveraging Ontologies for Lifted Probabilistic Inference and Learning (2010) (4)
- Using Structural Motifs for Learning Markov Logic Networks (2010) (4)
- Submodular Field Grammars: Representation, Inference, and Application to Image Parsing (2018) (3)
- Foundations of Adversarial Machine Learning (2007) (3)
- Learning Statistical Models of Time-Varying Relational Data (2003) (3)
- Knowledge Extraction and Joint Inference Using Tractable Markov Logic (2012) (3)
- Beyond Occam's Razor: Process-Oriented Evaluation (2000) (3)
- Ai will serve our species, not control it: our digital doubles. (2018) (3)
- Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning (2011) (3)
- Exploiting Context in Feature Selection (1996) (3)
- Research on Statistical Relational Learning at the University of Washington (2003) (2)
- Hypergraph Lifting for Structure Learning in Markov Logic Networks (2009) (2)
- Simple Bayesian Classifiers Do Not Assume Independence (1996) (2)
- Learning, Logic, and Probability: A Unified View (2004) (2)
- A mystery in the machine (2016) (2)
- Design and evaluation of the RISE 1.0 learning system (1994) (2)
- Data Integration: A "Killer App" for Multistrategy Learning (2000) (2)
- Toward Statistical Predicate Invention (2006) (2)
- Markov logic for machine reading (2011) (2)
- Tractable Markov Logic (2012) (2)
- Learning Multiple Hierarchical Relational Clusterings (2012) (2)
- Unifying Sum-Product Networks and Submodular Fields (2017) (2)
- From Instances to Rules: A Comparison of Biases (1996) (2)
- Multimodal Inductive Reasoning: Combining Rule-Based and Case-Based Learning (1998) (2)
- Mining Decision Trees from Streams (2016) (2)
- Recursive Decomposition for Nonconvex Optimization Supplementary Material (2015) (1)
- Submodular Sum-product Networks for Scene Understanding (2017) (1)
- Progressive rules: a method for representing and using real-time knowledge (1995) (1)
- Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning (2020) (1)
- Just add weights (2008) (1)
- Pedro Domingos on The Master Algorithm: A Conversation with Vasant Dhar (2016) (1)
- Deep Learning for Semantic Parsing (2009) (1)
- Nonconvex Optimization Is Combinatorial Optimization (2013) (0)
- Hybrid Marko v Logic Networks (2008) (0)
- A Comparison of Model Averaging Methods in Foreign Exchange Prediction (1997) (0)
- E 4 — Machine Learning (2007) (0)
- Inference in Dynamic Probabilistic Relational Models (2004) (0)
- Process-oriented evaluation: The next step (1999) (0)
- Unifying Sum-Product Networks and Submodular Fields ( Supplementary Material ) (2017) (0)
- Learning, logic, and probability (2006) (0)
- Multistrategy Learning: A Case Study (1996) (0)
- Knowledge collection from volunteer contributors : papers from the 2005 AAAI Symposium, March 21-23, Stanford, California (2005) (0)
- Information Integration Seedling for Data Integration and exploitation System that Learns (DIESEL) (2009) (0)
- Recursive Decomposition for Nonconvex Optimization - IJCAI-15 Distinguished Paper (2015) (0)
- Faust: Flexible Acquistion and Understanding System for Text (2013) (0)
- A Unified Approach to Abductive Inference (2014) (0)
- SEMEX: Mining for Personal Information Integration (0)
- Learning How to Edit Text (2000) (0)
- Towards fast hybrid deep kernel learning methods (2019) (0)
- Fast iscovery of Sim (1996) (0)
- Algorithms for Collective Knowledge Acquisition (2012) (0)
- Learning from Networks of Examples (2003) (0)
- Efficient learning and inference in rich statistical representations (2010) (0)
- Self-Supervised Object-Level Deep Reinforcement Learning (2020) (0)
- Transfer Learning in Integrated Cognitive Systems (2010) (0)
- Fast Discovery of Simple Rules (1996) (0)
- Towards a Unified A roach to Come (1999) (0)
- Sum-product networks (2011) (0)
- End-User Programming at the University of Washington (2006) (0)
- A General Method for S aling Up (2007) (0)
- Organizing committee (2011) (0)
- Statistical modeling of relational data (2007) (0)
- Chapter Eleven Learning Repetitive Text-Editing Procedures with SMARTedit Tessa Lau (2000) (0)
- Sum-Product Networks: The Next Generation of Deep Models (2017) (0)
- Machine Learning (2017) (0)
- Automated Debugging with Tractable Probabilistic Programming (2014) (0)
- Master Algorithms - Pedros Domingos (2020) (0)
- Exploiting Structure for Tractable Nonconvex Optimization (2014) (0)
- Reports on the 2005 AAAI Spring Symposium Series (2005) (0)

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Pedro Domingos is most known for their academic work in the field of computer science. They are also known for their academic work in the fields of

Pedro Domingos has made the following academic contributions:

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