Pedro Domingos

Q: What Schools Are Affiliated With Pedro Domingos

Pedro Domingos is affiliated with the following schools: University of California, Irvine, University of Washington

Pedro Domingos's AcademicInfluence.com Rankings

Pedro Domingos

Computer Science

#139

World Rank

#146

Historical Rank

Machine Learning

World Rank

Historical Rank

Database

#424

World Rank

#444

Historical Rank

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Computer Science

Pedro Domingos's Degrees

PhD Computer Science University of California, Irvine

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Why Is Pedro Domingos Influential?

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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.

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Pedro Domingos's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss (1997) (3256)
Markov logic networks (2006) (2924)
Mining the network value of customers (2001) (2866)
A few useful things to know about machine learning (2012) (2442)
Mining high-speed data streams (2000) (2220)
Mining time-changing data streams (2001) (1784)
Mining knowledge-sharing sites for viral marketing (2002) (1780)
MetaCost: a general method for making classifiers cost-sensitive (1999) (1569)
Learning to map between ontologies on the semantic web (2002) (1079)
Adversarial classification (2004) (929)
Reconciling schemas of disparate data sources: a machine-learning approach (2001) (908)
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier (1996) (895)
Sum-product networks: A new deep architecture (2011) (654)
Tree Induction for Probability-Based Ranking (2003) (589)
Learning to match ontologies on the Semantic Web (2003) (584)
Trust Management for the Semantic Web (2003) (564)
Ontology Matching: A Machine Learning Approach (2004) (564)
Markov Logic: An Interface Layer for Artificial Intelligence (2009) (462)
The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank (2001) (448)
iMAP: discovering complex semantic matches between database schemas (2004) (430)
Entity Resolution with Markov Logic (2006) (419)
The Role of Occam's Razor in Knowledge Discovery (1999) (417)
Learning the structure of Markov logic networks (2005) (348)
Social Networks Applied (2005) (340)
Learning Bayesian network classifiers by maximizing conditional likelihood (2004) (323)
The Alchemy System for Statistical Relational AI: User Manual (2007) (318)
Naive Bayes models for probability estimation (2005) (314)
Discriminative Training of Markov Logic Networks (2005) (303)
Sound and Efficient Inference with Probabilistic and Deterministic Dependencies (2006) (302)
Lifted First-Order Belief Propagation (2008) (298)
Joint Inference in Information Extraction (2007) (294)
Learning to Match the Schemas of Data Sources: A Multistrategy Approach (2003) (288)
Representing and reasoning about mappings between domain models (2002) (281)
Deep Symmetry Networks (2014) (252)
Joint Unsupervised Coreference Resolution with Markov Logic (2008) (245)
Efficient Weight Learning for Markov Logic Networks (2007) (245)
Statistical predicate invention (2007) (244)
Deep transfer via second-order Markov logic (2009) (239)
Mining Social Networks for Viral Marketing (238)
Learning the Structure of Sum-Product Networks (2013) (234)
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (2015) (225)
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering (2001) (221)
Probabilistic theorem proving (2011) (205)
Programming by Demonstration Using Version Space Algebra (2003) (200)
Relational Markov models and their application to adaptive web navigation (2002) (195)
Discriminative Learning of Sum-Product Networks (2012) (184)
Multi-Relational Record Linkage (2003) (183)
Catching up with the Data: Research Issues in Mining Data Streams (2001) (179)
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017) (179)
Bayesian Averaging of Classifiers and the Overfitting Problem (2000) (177)
Unifying instance-based and rule-based induction (2004) (174)
A Unified Bias-Variance Decomposition and its Applications (2000) (169)
A General Framework for Mining Massive Data Streams (2003) (168)
A Unified Bias-Variance Decomposition for Zero-One and Squared Loss (2000) (165)
A Unifeid Bias-Variance Decomposition and its Applications (2000) (155)
Personalizing web sites for mobile users (2001) (150)
Hybrid Markov Logic Networks (2008) (149)
Adaptive Web Navigation for Wireless Devices (2001) (147)
Unsupervised Ontology Induction from Text (2010) (145)
Structured machine learning: the next ten years (2008) (134)
Knowledge Discovery Via Multiple Models (1998) (132)
Automatically Personalizing User Interfaces (2003) (132)
Learning Markov logic network structure via hypergraph lifting (2009) (132)
Learning Markov Logic Networks Using Structural Motifs (2010) (130)
Rule Induction and Instance-Based Learning: A Unified Approach (1995) (130)
Extracting Semantic Networks from Text Via Relational Clustering (2008) (123)
iMAP: Discovering Complex Mappings between Database Schemas. (2004) (122)
Why Does Bagging Work? A Bayesian Account and its Implications (1997) (121)
1 Markov Logic: A Unifying Framework for Statistical Relational Learning (2010) (120)
Memory-Efficient Inference in Relational Domains (2006) (117)
Version Space Algebra and its Application to Programming by Demonstration (2000) (111)
Learning Source Description for Data Integration (2000) (110)
Knowledge Acquisition from Examples Via Multiple Models (1997) (107)
Learning Arithmetic Circuits (2008) (106)
A Unified Bias-Variance Decomposition (106)
Occam's Two Razors: The Sharp and the Blunt (1998) (104)
Markov Logic (2008) (102)
Learning with Knowledge from Multiple Experts (2003) (101)
Building large knowledge bases by mass collaboration (2003) (100)
Learning Source Descriptions for Data Integration (2000) (100)
On the Latent Variable Interpretation in Sum-Product Networks (2016) (97)
Control-Sensitive Feature Selection for Lazy Learners (1997) (96)
A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC (2008) (91)
Unifying Instance-Based and Rule-Based Induction (1996) (87)
Prospects and challenges for multi-relational data mining (2003) (87)
Markov Logic in Infinite Domains (2007) (86)
Object Identification with Attribute-Mediated Dependences (2005) (83)
Learning to map between structured representations of data (2002) (83)
Dynamic Probabilistic Relational Models (2003) (79)
On Theoretical Properties of Sum-Product Networks (2015) (78)
Mining complex models from arbitrarily large databases in constant time (2002) (73)
Toward knowledge-rich data mining (2007) (64)
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)
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) (53)
Bottom-Up Learning of Markov Network Structure (2010) (51)
Programming by demonstration: a machine learning approach (2001) (49)
Relational Dynamic Bayesian Networks (2005) (49)
Machine Reading at the University of Washington (2010) (44)
Knowledge Acquisition form Examples Vis Multiple Models (1997) (44)
Every Model Learned by Gradient Descent Is Approximately a Kernel Machine (2020) (44)
Mixed initiative interfaces for learning tasks: SMARTedit talks back (2001) (43)
Learning Efficient Markov Networks (2010) (43)
Learning Repetitive Text-Editing Procedures with SMARTedit (2001) (42)
A machine learning approach to web personalization (2002) (40)
Using Partitioning to Speed Up Specific-to-General Rule Induction (1996) (39)
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)
The RISE system: conquering without separating (1994) (36)
A Language for Relational Decision Theory (2009) (35)
Implementing Weighted Abduction in Markov Logic (2011) (35)
Learning from Infinite Data in Finite Time (2001) (35)
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)
The Sum-Product Theorem: A Foundation for Learning Tractable Models (2016) (32)
Linear-Time Rule Induction (1996) (31)
Efficient Lifting for Online Probabilistic Inference (2010) (30)
Exploiting Logical Structure in Lifted 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)
Learning Relational Sum-Product Networks (2015) (29)
A Process-Oriented Heuristic for Model Selection (1998) (29)
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models (2011) (26)
Formula-Based Probabilistic Inference (2010) (26)
Learning Tractable Probabilistic Models for Fault Localization (2015) (25)
Unifying logical and statistical AI with Markov logic (2019) (24)
Approximate Lifted Belief Propagation (2010) (23)
Machine Reading: A "Killer App" for Statistical Relational AI (2010) (22)
Recursive Random Fields (2007) (22)
Mining Massive Relational Databases (2003) (20)
Mining massive data streams (2005) (20)
Structured Message Passing (2013) (19)
Efficient Specific-to-General Rule Induction (1996) (19)
What ’ s Missing in AI : The Interface Layer (18)
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem (2017) (17)
Approximate Inference by Compilation to Arithmetic Circuits (2010) (16)
Process-Oriented Estimation of Generalization Error (1999) (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) (16)
The RISE 2.0 System: A Case Study in Multistrategy Learning (1995) (15)
Markov logic: a unifying language for knowledge and information management (2008) (15)
Exchangeable Variable Models (2014) (14)
When and how to subsample: report on the KDD-2001 panel (2002) (13)
“It’s Going to Kill Us!” and Other Myths About the Future of Artificial Intelligence (2016) (12)
Deep Transfer: A Markov Logic Approach (2011) (11)
Two-way induction (1995) (11)
Tractable Probabilistic Knowledge Bases with Existence Uncertainty (2013) (10)
Learning Mappings between Data Schemas (2000) (10)
Learning and Inference in Tractable Probabilistic Knowledge Bases (2015) (10)
Machine Learning for Data Management: Problems and Solutions (2018) (9)
Learning Models of Relational Stochastic Processes (2005) (9)
Real-World Learning with Markov Logic Networks (2004) (9)
Towards a Unified Approach to Concept Learning (1996) (9)
Symmetry-Based Semantic Parsing (2015) (9)
Learning and inference in collective knowledge bases (2004) (9)
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity (2020) (8)
Combining Link and Content Information in Web Search (2004) (7)
Markov Logic: A Language and Algorithms for Link Mining (2010) (6)
Collective Object Identification (2005) (6)
Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond (2014) (6)
Learning Multiple Models without Sacrificing Comprehensibility (1997) (6)
Structure learning in markov logic networks (2010) (6)
Compositional Kernel Machines (2017) (6)
Markov logic: theory, algorithms and applications (2009) (5)
Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition (2008) (5)
Learning Tractable Statistical Relational Models (2014) (5)
Building the Semantic Web by Mass Collaboration (2003) (5)
Bayesian Model Averaging in Rule Induction (1997) (5)
Foundations of Adversarial Machine Learning (2007) (4)
Mixed initiative interfaces for learning tasks (2001) (4)
Leveraging Ontologies for Lifted Probabilistic Inference and Learning (2010) (4)
Knowledge Extraction and Joint Inference Using Tractable Markov Logic (2012) (4)
Mining Decision Trees from Streams (2016) (3)
Learning Statistical Models of Time-Varying Relational Data (2003) (3)
Ai will serve our species, not control it: our digital doubles. (2018) (3)
Exploiting Context in Feature Selection (1996) (3)
Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning (2011) (3)
Submodular Field Grammars: Representation, Inference, and Application to Image Parsing (2018) (3)
Beyond Occam's Razor: Process-Oriented Evaluation (2000) (3)
Using Structural Motifs for Learning Markov Logic Networks (2010) (3)
A mystery in the machine (2016) (2)
Design and evaluation of the RISE 1.0 learning system (1994) (2)
Tractable Markov Logic (2012) (2)
Multimodal Inductive Reasoning: Combining Rule-Based and Case-Based Learning (1998) (2)
Research on Statistical Relational Learning at the University of Washington (2003) (2)
Simple Bayesian Classifiers Do Not Assume Independence (1996) (2)
Toward Statistical Predicate Invention (2006) (2)
Submodular Sum-product Networks for Scene Understanding (2017) (2)
Learning, Logic, and Probability: A Unified View (2004) (2)
Data Integration: A "Killer App" for Multistrategy Learning (2000) (2)
Hypergraph Lifting for Structure Learning in Markov Logic Networks (2009) (2)
Learning Multiple Hierarchical Relational Clusterings (2012) (2)
From Instances to Rules: A Comparison of Biases (1996) (2)
Unifying Sum-Product Networks and Submodular Fields (2017) (2)
Markov logic for machine reading (2011) (2)
Progressive rules: a method for representing and using real-time knowledge (1995) (1)
Deep Learning for Semantic Parsing (2009) (1)
Just add weights (2008) (1)
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning (2020) (1)
Reports on the 2005 AAAI Spring Symposium Series (2005) (1)
Recursive Decomposition for Nonconvex Optimization Supplementary Material (2015) (1)
Pedro Domingos on The Master Algorithm: A Conversation with Vasant Dhar (2016) (1)
Master Algorithms - Pedros Domingos (2020) (0)
Hybrid Marko v Logic Networks (2008) (0)
Algorithms for Collective Knowledge Acquisition (2012) (0)
Towards a Unified A roach to Come (1999) (0)
A Unified Approach to Abductive Inference (2014) (0)
Multistrategy Learning: A Case Study (1996) (0)
Fast iscovery of Sim (1996) (0)
Sum-product networks (2011) (0)
Fast Discovery of Simple Rules (1996) (0)
Organizing committee (2011) (0)
Exploiting Structure for Tractable Nonconvex Optimization (2014) (0)
Information Integration Seedling for Data Integration and exploitation System that Learns (DIESEL) (2009) (0)
Learning How to Edit Text (2000) (0)
Nonconvex Optimization Is Combinatorial Optimization (2013) (0)
Learning, logic, and probability (2006) (0)
Automated Debugging with Tractable Probabilistic Programming (2014) (0)
Knowledge collection from volunteer contributors : papers from the 2005 AAAI Symposium, March 21-23, Stanford, California (2005) (0)
Efficient learning and inference in rich statistical representations (2010) (0)
Chapter Eleven Learning Repetitive Text-Editing Procedures with SMARTedit Tessa Lau (2000) (0)
Towards fast hybrid deep kernel learning methods (2019) (0)
Unifying Sum-Product Networks and Submodular Fields ( Supplementary Material ) (2017) (0)
Faust: Flexible Acquistion and Understanding System for Text (2013) (0)
Machine Learning (2017) (0)
Self-Supervised Object-Level Deep Reinforcement Learning (2020) (0)
Learning from Networks of Examples (2003) (0)
Statistical modeling of relational data (2007) (0)
Recursive Decomposition for Nonconvex Optimization - IJCAI-15 Distinguished Paper (2015) (0)
Sum-Product Networks: The Next Generation of Deep Models (2017) (0)
Inference in Dynamic Probabilistic Relational Models (2004) (0)
SEMEX: Mining for Personal Information Integration (0)
A Comparison of Model Averaging Methods in Foreign Exchange Prediction (1997) (0)
E 4 — Machine Learning (2007) (0)
Transfer Learning in Integrated Cognitive Systems (2010) (0)
End-User Programming at the University of Washington (2006) (0)
Session details: Link Analysis (2002) (0)
A General Method for S aling Up (2007) (0)
Process-oriented evaluation: The next step (1999) (0)