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Foster Provost - Publications

Affiliations: 
New York University, Graduate School of Business Administration 
Area:
Computer Science
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46 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2020 Ramon Y, Martens D, Provost F, Evgeniou T. A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C Advanced Data Analysis and Classification. DOI: 10.1007/S11634-020-00418-3  0.726
2020 Stankova M, Martens D, Provost F. Classification over bipartite graphs through projection Machine Learning. 1-51. DOI: 10.1007/S10994-020-05898-0  0.649
2019 De Cnudde S, Ramon Y, Martens D, Provost F. Deep Learning on Big, Sparse, Behavioral Data. Big Data. 7: 286-307. PMID 31860341 DOI: 10.1089/big.2019.0095  0.689
2018 Cohen MC, Guetta CD, Jiao K, Provost F. Data-Driven Investment Strategies for Peer-to-Peer Lending: A Case Study for Teaching Data Science. Big Data. 6: 191-213. PMID 30283728 DOI: 10.1089/big.2018.0092  0.386
2016 Martens D, Provost F, Clark J, Fortuny EJd. Mining massive fine-grained behavior data to improve predictive analytics Management Information Systems Quarterly. 40: 869-888. DOI: 10.25300/Misq/2016/40.4.04  0.704
2015 Dalessandro B, Hook R, Perlich C, Provost F. Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies. Big Data. 3: 90-102. PMID 27447433 DOI: 10.1089/big.2015.0006  0.712
2015 Provost F, Martens D, Murray A. Finding similar mobile consumers with a privacy-friendly geosocial design Information Systems Research. 26: 243-265. DOI: 10.1287/Isre.2015.0576  0.63
2015 Attenberg J, Ipeirotis P, Provost F. Beat the machine: Challenging humans to find a predictive model's "unknown unknowns", Journal of Data and Information Quality. 6. DOI: 10.1145/2700832  0.375
2015 De Fortuny EJ, Evgeniou T, Martens D, Provost F. Iteratively refining SVMs using priors Proceedings - 2015 Ieee International Conference On Big Data, Ieee Big Data 2015. 46-52. DOI: 10.1109/BigData.2015.7363740  0.704
2014 Provost F, Webb GI, Bekkerman R, Etzioni O, Fayyad U, Perlich C. A Data Scientist's Guide to Start-Ups. Big Data. 2: 117-28. PMID 27442492 DOI: 10.1089/big.2014.0031  0.688
2014 Martens D, Provost F. Explaining data-driven document classifications Mis Quarterly: Management Information Systems. 38: 73-99. DOI: 10.25300/Misq/2014/38.1.04  0.713
2014 Williams MH, Perlich C, Dalessandro B, Provost F. Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground truth Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. DOI: 10.1145/2648584.2648587  0.676
2014 Dalessandro B, Chen D, Raeder T, Perlich C, Han Williams M, Provost F. Scalable hands-free transfer learning for online advertising Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 1573-1582. DOI: 10.1145/2623330.2623349  0.664
2014 Perlich C, Dalessandro B, Raeder T, Stitelman O, Provost F. Machine learning for targeted display advertising: Transfer learning in action Machine Learning. 95: 103-127. DOI: 10.1007/S10994-013-5375-2  0.722
2014 Ipeirotis PG, Provost F, Sheng VS, Wang J. Repeated labeling using multiple noisy labelers Data Mining and Knowledge Discovery. 28: 402-441. DOI: 10.1007/S10618-013-0306-1  0.329
2013 Junqué de Fortuny E, Martens D, Provost F. Predictive Modeling With Big Data: Is Bigger Really Better? Big Data. 1: 215-26. PMID 27447254 DOI: 10.1089/big.2013.0037  0.723
2012 Dalessandro B, Stitelman O, Perlich C, Provost F. Causally motivated attribution for online advertising Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. DOI: 10.1145/2351356.2351363  0.666
2012 Raeder T, Stitelman O, Dalessandro B, Perlich C, Provost F. Design principles of massive, robust prediction systems Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 1357-1365. DOI: 10.1145/2339530.2339740  0.73
2012 Perlich C, Dalessandro B, Hook R, Stitelman O, Raeder T, Provost F. Bid optimizing and inventory scoring in targeted online advertising Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 804-812. DOI: 10.1145/2339530.2339655  0.686
2011 Attenberg J, Provost F. Online active inference and learning Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 186-194. DOI: 10.1145/2020408.2020443  0.308
2011 Attenberg J, Provost F. Inactive learning? Acm Sigkdd Explorations Newsletter. 12: 36-41. DOI: 10.1145/1964897.1964906  0.357
2009 Saar-Tsechansky M, Melville P, Provost F. Active feature-value acquisition Management Science. 55: 664-684. DOI: 10.1287/Mnsc.1080.0952  0.75
2007 Saar-Tsechansky M, Provost F. Decision-centric active learning of binary-outcome models Information Systems Research. 18: 4-22. DOI: 10.1287/Isre.1070.0111  0.743
2007 Provost F, Melville P, Saar-Tsechansky M. Data acquisition and cost-effective predictive modeling: Targeting offers for electronic commerce Acm International Conference Proceeding Series. 258: 389-398. DOI: 10.1145/1282100.1282172  0.738
2007 Saar-Tsechansky M, Provost F. Handling missing values when applying classification models Journal of Machine Learning Research. 8: 1625-1657.  0.719
2006 Hill S, Provost F, Volinsky C. Network-based marketing: Identifying likely adopters via consumer networks Statistical Science. 21: 256-276. DOI: 10.1214/088342306000000222  0.315
2006 Perlich C, Provost F. Distribution-based aggregation for relational learning with identifier attributes Machine Learning. 62: 65-105. DOI: 10.1007/S10994-006-6064-1  0.723
2005 Melville P, Saar-Tsechansky M, Provost F, Mooney R. Economical active feature-value acquisition through expected utility estimation Proceedings of the 1st International Workshop On Utility-Based Data Mining, Ubdm '05. 10-16. DOI: 10.1145/1089827.1089828  0.722
2005 Bernstein A, Provost F, Hill S. Toward intelligent assistance for a data mining process: An ontology-based approach for cost-sensitive classification Ieee Transactions On Knowledge and Data Engineering. 17: 503-518. DOI: 10.1109/Tkde.2005.67  0.411
2005 Melville P, Saar-Tsechansky M, Provost F, Mooney R. An expected utility approach to active feature-value acquisition Proceedings - Ieee International Conference On Data Mining, Icdm. 745-748. DOI: 10.1109/ICDM.2005.23  0.727
2004 Perlich C, Provost F, Simonoff JS. Tree induction vs. Logistic regression: A learning-curve analysis Journal of Machine Learning Research. 4: 211-255. DOI: 10.1162/153244304322972694  0.727
2004 Melville P, Saar-Tsechansky M, Provost F, Mooney R. Active feature-value acquisition for classifier induction Proceedings - Fourth Ieee International Conference On Data Mining, Icdm 2004. 483-486. DOI: 10.1109/ICDM.2004.10075  0.734
2004 Saar-Tsechansky M, Provost F. Active Sampling for Class Probability Estimation and Ranking Machine Learning. 54: 153-178. DOI: 10.1023/B:MACH.0000011806.12374.c3  0.728
2004 Kolluri V, Provost F, Buchanan B, Metzler D. Knowledge discovery using concept-class taxonomies Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 3339: 450-461.  0.35
2003 Weiss GM, Provost F. Learning when training data are costly: The effect of class distribution on tree induction Journal of Artificial Intelligence Research. 19: 315-354. DOI: 10.1613/Jair.1199  0.387
2003 Perlich C, Provost F, Macskassy S. Predicting citation rates for physics papers Acm Sigkdd Explorations Newsletter. 5: 154-155. DOI: 10.1145/980972.980994  0.678
2003 Perlich C, Provost F. Aggregation-based feature invention and relational concept classes Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 167-176. DOI: 10.1145/956750.956772  0.716
2003 Provost F, Domingos P. Tree induction for probability-based ranking Machine Learning. 52: 199-215. DOI: 10.1023/A:1024099825458  0.382
2001 Provost F, Fawcett T. Robust classification for imprecise environments Machine Learning. 42: 203-231. DOI: 10.1023/A:1007601015854  0.413
2001 Saar-Tsechansky M, Provost F. Active learning for class probability estimation and ranking Ijcai International Joint Conference On Artificial Intelligence. 911-917.  0.725
2000 Dhar V, Chou D, Provost F. Discovering interesting patterns for investment decision making with GLOWER - A genetic learner overlaid with entropy reduction Data Mining and Knowledge Discovery. 4: 69-80. DOI: 10.1023/A:1009848126475  0.437
1999 Aha DW, Daniels JJ, Sahami M, Danyluk A, Fawcett T, Provost F, Logan B, Baxter J. AAAI-98 Workshops: Reports of the Workshops Held at the Fifteenth National Conference on Artificial Intelligence in Madison, Wisconsin Ai Magazine. 20: 123. DOI: 10.1609/Aimag.V20I1.1446  0.35
1999 Provost F, Kolluri V. A survey of methods for scaling up inductive algorithms Data Mining and Knowledge Discovery. 3: 131-169. DOI: 10.1023/A:1009876119989  0.366
1999 Provost F, Danyluk AP. Problem definition, data cleaning, and evaluation: a classifier learning case study Informatica (Ljubljana). 23: 123-136.  0.307
1998 Provost F, Kohavi R. Guest editors' introduction: On applied research in machine learning Machine Learning. 30: 127-132. DOI: 10.1023/A:1007442505281  0.361
1994 Provost FJ, Hennessy DN. Distributed machine learning: scaling up with coarse-grained parallelism Proceedings / . International Conference On Intelligent Systems For Molecular Biology ; Ismb. International Conference On Intelligent Systems For Molecular Biology. 2: 340-347. PMID 7584410  0.335
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