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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 DBLP: Provost:Foster_J=

Facilitated 1 volumes:

KDD 2001Ed

Contributed to:

KDD 20152015
KDD 20142014
KDD 20132013
KDD 20122012
KDD 20112011
KDD 20102010
KDD 20092009
KDD 20082008
ICML 20052005
KDD 20032003
SIGIR 20012001
KDD 19991999
ICML 19981998
KDD 19971997
KDD 19961996
KDD 19941994
ICML 19931993

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.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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