BibSLEIGH — Pedro_M

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:

2014

2013

2010

2009

2008

2007

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

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.