Travelled to:1 × Australia
1 × China
9 × USA
Collaborated with:D.Jensen M.Hay H.Eldardiry A.Kuwadekar M.Yakout A.K.Elmagarmid M.Ouzzani J.J.P.III P.N.Bennett S.Moreno S.Kirshner C.Mayfield S.Prabhakar B.Gallagher N.K.Ahmed N.G.Duffield R.R.Kompella L.Friedland I.F.Ilyas Ö.Simsek J.Komoroske K.Palmer H.G.Goldberg
Talks about:relat (6) learn (5) graph (4) data (4) collect (3) infer (3) classif (2) repair (2) improv (2) model (2)
Person: Jennifer Neville
DBLP: Neville:Jennifer
Contributed to:
2014
201420132012
2011
2011
201020052004200320032002Wrote 13 papers:
- CIKM-2014-PfeifferNB #learning #network #probability #using
- Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference (JJPI, JN, PNB), pp. 639–648.
- KDD-2014-AhmedDNK #framework #graph
- Graph sample and hold: a framework for big-graph analytics (NKA, NGD, JN, RRK), pp. 1446–1455.
- KDD-2013-MorenoNK #graph #learning #modelling
- Learning mixed kronecker product graph models with simulated method of moments (SM, JN, SK), pp. 1052–1060.
- CIKM-2012-EldardiryN #analysis #classification #graph #how #predict
- An analysis of how ensembles of collective classifiers improve predictions in graphs (HE, JN), pp. 225–234.
- ICML-2011-KuwadekarN #classification #learning #modelling #relational
- Relational Active Learning for Joint Collective Classification Models (AK, JN), pp. 385–392.
- VLDB-2011-YakoutENOI #program repair
- Guided data repair (MY, AKE, JN, MO, IFI), pp. 279–289.
- SIGMOD-2010-MayfieldNP #approach #database #named #statistics
- ERACER: a database approach for statistical inference and data cleaning (CM, JN, SP), pp. 75–86.
- SIGMOD-2010-YakoutENO #named #program repair
- GDR: a system for guided data repair (MY, AKE, JN, MO), pp. 1223–1226.
- KDD-2005-NevilleSJKPG #information management #relational #using
- Using relational knowledge discovery to prevent securities fraud (JN, ÖS, DJ, JK, KP, HGG), pp. 449–458.
- KDD-2004-JensenNG #classification #relational #why
- Why collective inference improves relational classification (DJ, JN, BG), pp. 593–598.
- ICML-2003-JensenNH #bias #relational
- Avoiding Bias when Aggregating Relational Data with Degree Disparity (DJ, JN, MH), pp. 274–281.
- KDD-2003-NevilleJFH #learning #probability #relational
- Learning relational probability trees (JN, DJ, LF, MH), pp. 625–630.
- ICML-2002-JensenN #bias #feature model #learning #relational
- Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning (DJ, JN), pp. 259–266.