BibSLEIGH — Foster_J

Travelled to:

1 × Australia
1 × China
1 × France
1 × Germany
13 × USA

Collaborated with:

T.Fawcett C.Perlich J.M.Aronis B.Dalessandro J.Attenberg T.Raeder O.Stitelman R.Hook S.A.Macskassy V.Kolluri A.P.Danyluk V.S.Sheng P.G.Ipeirotis S.Rosset D.Jensen T.Oates R.Kohavi B.G.Buchanan X.Zhang A.Murray H.Hirsh R.Sankaranarayanan V.Dhar D.N.Hill R.Moakler A.E.Hubbard V.Tsemekhman K.Tsemekhman D.Chen M.H.Williams E.J.d.Fortuny M.Stankova J.Moeyersoms B.Minnaert D.Martens

Talks about:

learn (6) advertis (5) onlin (5) data (4) algorithm (3) network (3) induct (3) effici (3) label (3) class (3)

Person: Foster J. Provost

DBLP: Provost:Foster_J=

Facilitated 1 volumes:

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2005

2003

2001

1999

1998

1997

1996

1994

1993

Wrote 24 papers:

KDD-2015-HillMHTPT #online: Measuring Causal Impact of Online Actions via Natural Experiments: Application to Display Advertising (DNH, RM, AEH, VT, FJP, KT), pp. 1839–1847.
KDD-2014-DalessandroCRPWP #learning #online #scalability: Scalable hands-free transfer learning for online advertising (BD, DC, TR, CP, MHW, FJP), pp. 1573–1582.
KDD-2014-FortunySMMPM #detection: Corporate residence fraud detection (EJdF, MS, JM, BM, FJP, DM), pp. 1650–1659.
KDD-2013-RaederPDSP #clustering #reduction #scalability #using: Scalable supervised dimensionality reduction using clustering (TR, CP, BD, OS, FJP), pp. 1213–1221.
KDD-2013-StitelmanPDHRP #detection #network #online #scalability #using: Using co-visitation networks for detecting large scale online display advertising exchange fraud (OS, CP, BD, RH, TR, FJP), pp. 1240–1248.
KDD-2012-PerlichDHSRP #online #optimisation: Bid optimizing and inventory scoring in targeted online advertising (CP, BD, RH, OS, TR, FJP), pp. 804–812.
KDD-2012-RaederSDPP #design #predict #robust: Design principles of massive, robust prediction systems (TR, OS, BD, CP, FJP), pp. 1357–1365.
KDD-2011-AttenbergP #learning #online: Online active inference and learning (JA, FJP), pp. 186–194.
KDD-2010-AttenbergP #classification #learning #modelling #why: Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance (JA, FJP), pp. 423–432.
KDD-2009-ProvostDHZM #network #online #privacy #social: Audience selection for on-line brand advertising: privacy-friendly social network targeting (FJP, BD, RH, XZ, AM), pp. 707–716.
KDD-2008-ShengPI #data mining #mining #multi #quality #using: Get another label? improving data quality and data mining using multiple, noisy labelers (VSS, FJP, PGI), pp. 614–622.
ICML-2005-MacskassyPR #empirical #evaluation: ROC confidence bands: an empirical evaluation (SAM, FJP, SR), pp. 537–544.
KDD-2003-PerlichP #concept #relational: Aggregation-based feature invention and relational concept classes (CP, FJP), pp. 167–176.
SIGIR-2001-MacskassyHPSD: Intelligent Information Triage (SAM, HH, FJP, RS, VD), pp. 318–326.
KDD-1999-FawcettP #behaviour #monitoring #process: Activity Monitoring: Noticing Interesting Changes in Behavior (TF, FJP), pp. 53–62.
KDD-1999-ProvostJO #performance: Efficient Progressive Sampling (FJP, DJ, TO), pp. 23–32.
ICML-1998-ProvostFK #algorithm #estimation #induction: The Case against Accuracy Estimation for Comparing Induction Algorithms (FJP, TF, RK), pp. 445–453.
KDD-1997-AronisP #algorithm #data mining #mining #performance: Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation (JMA, FJP), pp. 119–122.
KDD-1997-ProvostF #analysis #classification #comparison #performance #visualisation: Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions (FJP, TF), pp. 43–48.
KDD-1997-ProvostK #algorithm #bibliography #induction #perspective #scalability: Scaling Up Inductive Algorithms: An Overview (FJP, VK), pp. 239–242.
KDD-1996-AronisPB #automation: Exploiting Background Knowledge in Automated Discovery (JMA, FJP, BGB), pp. 355–358.
KDD-1996-FawcettP #data mining #effectiveness #machine learning #mining #profiling: Combining Data Mining and Machine Learning for Effective User Profiling (TF, FJP), pp. 8–13.
KDD-1994-AronisP #induction #machine learning #relational: Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning (JMA, FJP), pp. 347–358.
ICML-1993-DanylukP #fault #learning #network: Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network (APD, FJP), pp. 81–88.