Travelled to:1 × Australia
1 × The Netherlands
16 × USA
3 × Portugal
Collaborated with:J.Neville ∅ T.Oates L.Friedland M.J.Rattigan M.E.Maier M.Hay W.B.Croft M.D.Schmill A.McGovern A.S.Fast H.G.Goldberg M.Lavine T.Strohman C.Shah B.N.Levine B.Gallagher H.Blau F.J.Provost J.Komoroske G.Miklau D.F.Towsley P.Weis C.Faloutsos Ö.Simsek K.Palmer V.Lavrenko D.Lawrie P.Ogilvie J.Allan R.Kumar A.Tuzhilin G.Kossinets J.Leskovec A.Tomkins B.J.Taylor T.E.Senator A.Memory W.T.Young B.Rees R.Pierce D.Huang M.Reardon D.A.Bader E.Chow I.A.Essa J.Jones V.Bettadapura D.H.Chau O.Green O.Kaya A.Zakrzewska E.Briscoe R.L.M.IV R.McColl L.Weiss T.G.Dietterich A.Fern W.Wong S.Das A.Emmott J.Irvine J.Y.Lee D.Koutra D.D.Corkill A.Gentzel
Talks about:relat (7) network (6) discoveri (4) structur (4) knowledg (4) social (4) detect (4) improv (3) design (3) model (3)
Person: David Jensen
DBLP: Jensen:David
Contributed to:
2013
2013
2009
2009
20092008
200820072007
2007
200620062005200420032003200220001999199819971997Wrote 26 papers:
- ICML-c3-2013-FriedlandJL #detection #social
- Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events (LF, DJ, ML), pp. 1175–1183.
- KDD-2013-SenatorGMYRPHRBCEJBCGKZBMMWDFWDEILKFCFGJ #database #detection #process
- Detecting insider threats in a real corporate database of computer usage activity (TES, HGG, AM, WTY, BR, RP, DH, MR, DAB, EC, IAE, JJ, VB, DHC, OG, OK, AZ, EB, RLMI, RM, LW, TGD, AF, WKW, SD, AE, JI, JYL, DK, CF, DDC, LF, AG, DJ), pp. 1393–1401.
- KDIR-2009-Jensen #design #information management
- Knowledge Discovery by Design (DJ), p. 9.
- KEOD-2009-Jensen #design #information management
- Knowledge Discovery by Design (DJ), p. 9.
- KMIS-2009-Jensen #design #information management
- Knowledge Discovery by Design (DJ), p. 9.
- KDD-2008-KumarTFJKLT #network #social
- Social networks: looking ahead (RK, AT, CF, DJ, GK, JL, AT), p. 1060.
- VLDB-2008-HayMJTW #identification #network #social
- Resisting structural re-identification in anonymized social networks (MH, GM, DJ, DFT, PW), pp. 102–114.
- ICML-2007-RattiganMJ #clustering #graph #network
- Graph clustering with network structure indices (MJR, MEM, DJ), pp. 783–790.
- KDD-2007-FastFMTJGK #detection #preprocessor #relational
- Relational data pre-processing techniques for improved securities fraud detection (ASF, LF, MEM, BJT, DJ, HGG, JK), pp. 941–949.
- KDD-2007-FriedlandJ #identification
- Finding tribes: identifying close-knit individuals from employment patterns (LF, DJ), pp. 290–299.
- SIGIR-2007-StrohmanCJ #recommendation
- Recommending citations for academic papers (TS, WBC, DJ), pp. 705–706.
- CIKM-2006-ShahCJ #detection #documentation #representation
- Representing documents with named entities for story link detection (SLD) (CS, WBC, DJ), pp. 868–869.
- KDD-2006-RattiganMJ #approximate #network #performance #using
- Using structure indices for efficient approximation of network properties (MJR, MEM, DJ), pp. 357–366.
- KDD-2005-FastJL #network #peer-to-peer #social
- Creating social networks to improve peer-to-peer networking (ASF, DJ, BNL), pp. 568–573.
- 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.
- ICML-2003-McGovernJ #identification #learning #multi #predict #relational #using
- Identifying Predictive Structures in Relational Data Using Multiple Instance Learning (AM, DJ), pp. 528–535.
- KDD-2003-JensenRB #assessment
- Information awareness: a prospective technical assessment (DJ, MJR, HB), pp. 378–387.
- 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.
- CIKM-2000-LavrenkoSLOJA #modelling #recommendation
- Language Models for Financial News Recommendation (VL, MDS, DL, PO, DJ, JA), pp. 389–396.
- KDD-1999-ProvostJO #performance
- Efficient Progressive Sampling (FJP, DJ, TO), pp. 23–32.
- KDD-1998-OatesJ #dataset #modelling #scalability
- Large Datasets Lead to Overly Complex Models: An Explanation and a Solution (TO, DJ), pp. 294–298.
- ICML-1997-OatesJ #complexity #set
- The Effects of Training Set Size on Decision Tree Complexity (TO, DJ), pp. 254–262.
- KDD-1997-JensenS #multi
- Adjusting for Multiple Comparisons in Decision Tree Pruning (DJ, MDS), pp. 195–198.