`Travelled to:`

1 × China

1 × France

1 × Germany

1 × Israel

1 × Italy

15 × USA

3 × Canada

`Collaborated with:`

∅ S.Kok G.Hulten M.Richardson J.Davis D.S.Weld M.Niepert R.Gens D.Lowd D.Grossman M.J.Pazzani C.R.Anderson L.Spencer T.A.Lau N.N.Dalvi Mausam S.K.Sanghai D.Verma R.Dhamankar Y.Lee A.Doan A.Y.Halevy

`Talks about:`

learn (8) network (7) markov (7) model (6) structur (5) logic (5) mine (5) knowledg (4) classifi (4) bayesian (4)

## Person: Pedro M. Domingos

### DBLP: Domingos:Pedro_M=

### Facilitated 1 volumes:

### Contributed to:

### Wrote 32 papers:

- ICML-c2-2014-NiepertD #modelling
- Exchangeable Variable Models (MN, PMD), pp. 271–279.
- ICML-c3-2013-GensD #learning #network
- Learning the Structure of Sum-Product Networks (RG, PMD), pp. 873–880.
- ICML-2010-DavisD #bottom-up #learning #markov #network
- Bottom-Up Learning of Markov Network Structure (JD, PMD), pp. 271–278.
- ICML-2010-KokD #learning #logic #markov #network #using
- Learning Markov Logic Networks Using Structural Motifs (SK, PMD), pp. 551–558.
- ICML-2009-DavisD #higher-order #logic #markov
- Deep transfer via second-order Markov logic (JD, PMD), pp. 217–224.
- ICML-2009-KokD #learning #logic #markov #network
- Learning Markov logic network structure via hypergraph lifting (SK, PMD), pp. 505–512.
- CIKM-2008-Domingos #information management #logic #markov
- Markov logic: a unifying language for knowledge and information management (PMD), p. 519.
- ICML-2007-KokD #statistics
- Statistical predicate invention (SK, PMD), pp. 433–440.
- ICML-2005-KokD #learning #logic #markov #network
- Learning the structure of Markov logic networks (SK, PMD), pp. 441–448.
- ICML-2005-LowdD #estimation #modelling #naive bayes #probability
- Naive Bayes models for probability estimation (DL, PMD), pp. 529–536.
- ICML-2004-GrossmanD #classification #learning #network
- Learning Bayesian network classifiers by maximizing conditional likelihood (DG, PMD).
- KDD-2004-DalviDMSV #classification
- Adversarial classification (NND, PMD, M, SKS, DV), pp. 99–108.
- SIGMOD-2004-LeeDDHD #database #named
- iMAP: Discovering Complex Mappings between Database Schemas (RD, YL, AD, AYH, PMD), pp. 383–394.
- ICML-2003-RichardsonD #learning #multi
- Learning with Knowledge from Multiple Experts (MR, PMD), pp. 624–631.
- KDD-2002-AndersonDW #adaptation #markov #modelling #navigation #relational #web
- Relational Markov models and their application to adaptive web navigation (CRA, PMD, DSW), pp. 143–152.
- KDD-2002-HultenD #constant #database #mining #modelling #scalability
- Mining complex models from arbitrarily large databases in constant time (GH, PMD), pp. 525–531.
- KDD-2002-RichardsonD #mining
- Mining knowledge-sharing sites for viral marketing (MR, PMD), pp. 61–70.
- ICML-2001-DomingosH #algorithm #clustering #machine learning #scalability
- A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering (PMD, GH), pp. 106–113.
- KDD-2001-DomingosR #mining #network
- Mining the network value of customers (PMD, MR), pp. 57–66.
- KDD-2001-HultenSD #data type #mining
- Mining time-changing data streams (GH, LS, PMD), pp. 97–106.
- ICML-2000-Domingos #classification #problem
- Bayesian Averaging of Classifiers and the Overfitting Problem (PMD), pp. 223–230.
- ICML-2000-Domingos00a #composition
- A Unifeid Bias-Variance Decomposition and its Applications (PMD), pp. 231–238.
- ICML-2000-LauDW #algebra #programming
- Version Space Algebra and its Application to Programming by Demonstration (TAL, PMD, DSW), pp. 527–534.
- KDD-2000-DomingosH #data type #mining #performance
- Mining high-speed data streams (PMD, GH), pp. 71–80.
- KDD-1999-Domingos #classification #named
- MetaCost: A General Method for Making Classifiers Cost-Sensitive (PMD), pp. 155–164.
- ICML-1998-Domingos #heuristic
- A Process-Oriented Heuristic for Model Selection (PMD), pp. 127–135.
- KDD-1998-Domingos
- Occam’s Two Razors: The Sharp and the Blunt (PMD), pp. 37–43.
- ICML-1997-Domingos #information management #modelling #multi
- Knowledge Acquisition form Examples Vis Multiple Models (PMD), pp. 98–106.
- KDD-1997-Domingos #why
- Why Does Bagging Work? A Bayesian Account and its Implications (PMD), pp. 155–158.
- ICML-1996-DomingosP #classification #independence
- Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier (PMD, MJP), pp. 105–112.
- KDD-1996-Domingos #induction #linear
- Linear-Time Rule Induction (PMD), pp. 96–101.
- KDD-1996-Domingos96a #induction #performance
- Efficient Specific-to-General Rule Induction (PMD), pp. 319–322.