Foster Provost

This page is old, clunky, and no longer updated. 
Please see Foster's current homepage instead.

Ira Rennert Professor of Entrepreneurship
Professor of Data Science
Professor of Information Systems
Andre Meyer Faculty Fellow
Paduano Fellow in Business Ethics (Emeritus)

Affiliated Appointment: Department of Computer Science
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Curriculum Vitae

Some things that might interest you:

Prof. Provost interviewed on Data Science, Machine Learning, AI and Management.

European Research Paper of the Year 2017!  Here it is:

• Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics.  Martens, D., F. Provost, J. Clark, E. Junqué de Fortuny.  MIS Quarterly, Vol. 40 No. 4, pp. 869-888/December 2016

Professor Provost received the Best Paper Award for the best paper published in the journal Information Systems Research in 2015.  Here it is:

• Finding Mobile Consumers with a Privacy-Friendly Geo-Similarity Network.  F. Provost, D. Martens and A. Murray.  Information Systems Research 26(2), 243-265, 2015.  Best Paper Award.  Also INFORMS President's Pick.

How can you explain the decisions made by a predictive model?

• Explaining Data-driven Document Classifications.  Martens, D. and F. Provost.  MIS Quarterly 38(1), 73-99, 2014

Professor Provost won the Best Teacher Award from the Stern MSBA Class of 2014.  (Thanks!)

Data Science for Business (the book) has been the best-selling data science book for several years now!      

Tutorial on Predictive Modeling with Social Networks (ICWSM 2009) [prior version @ KDD 2008]

Privacy-friendly on-line brand advertising @ KDD 2009

Repeated labeling -- Best paper award runner-up @ KDD 2008

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Note to prospective Ph.D. students, postdocs, summer interns, etc.

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Prof. Provost retired in 2010 from being Editor-in-Chief of the journal Machine Learning.

Prof. Provost won the 2009 INFORMS Design Science Award for his work on Social Network-based Marketing Systems. Previously he received IBM Faculty Awards for outstanding research in data mining and machine learning.  He was elected as a founding board member of the International Machine Learning Society. He is a member of the editorial boards of the journals Machine Learning and Data Mining and Knowledge Discovery.  In 2001, he co-chaired the program of the premier data mining conference (ACM SIGKDD ).   (More bio info)

Prof. Provost co-founded of several successful NYC-based companies, including Detectica, Dstillery (formerly Media6degrees), Integral Ad Science, and Everyscreen Media.  He advises other companies on data science and strategy. 

Publications

Music

Bio
Contact Info
Students

Jefferson Provost
Caterina Provost-Smith

Some Current Students

Some Prior Students

Maytal Saar-Tsechansky, Associate Professor, Univ. Texas at Austin
Gary Weiss, Associate Professor, Fordham University (Ph.D. from Rutgers University, Computer Science; Co-advised with Haym Hirsh)
Claudia Perlich, Senior Data Scientist, TwoSigma  (Formerly at IBM Research and Chief Scientist, Dstillery)
Shawndra Hill, Senior Researcher, Microsoft Research (Formerly at Wharton)
Brian Dalessandro, Director, Data Science, Zocdoc (Formerly: Director, Data Science, Zocdoc; Vice President Research/Data Science, Dstillery)
Josh Attenberg,  Head of Data Science, Urbint; CoFounder Detectica (Formerly: Data Science Lead, Etsy)
Xiaohan Zhang, Director, Data Science, Integral Ad Science
Enric Junqué de Fortuny, Assistant Professor, NYU Shanghai (Formerly: Assistant Professor, Rotterdam School of Management; Senior Research Fellow, INSEAD; Ph.D. from U. Antwerp; Co-advised with David Martens)
Jessica Clark, Assistant Professor, Univ. Maryland College Park
Rob Moakler, Quantitative Researcher, Facebook

Prior Postdocs

Sofus Macskassy, Head of Data Analytics, Branch Metrics (Formerly: Director, Fetch Labs @ Fetch Technologies, and also Manager, Applied Machine Learning, Facebook and Assistant Adjunct Professor, USC)
Victor (Shengli) Sheng,  Associate Professor, University of Central Arkansas

Publications

The Book

• Data Science for Business: What you need to know about data mining and data analytic thinking.  F. Provost and T. Fawcett (2013).

Latest Music Album

• Mean Reversion. (Progressive Rock)  C. Hurvich and F. Provost.  Released October 2018 by Kloster Art Music. 

Recent Working Papers

• Causal Classification: Treatment effect estimation vs. outcome estimation. C. Fernandez & F. Provost.

• Implications of Confounding for Large-Scale Decision-making.  C. Fernandez & F. Provost. 

• Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach.  C. Fernandez, F. Provost & X. Ha.

• Methods for Individual Treatment Assignment: An Application and Comparison for Playlist Generation.  C. Fernandez, F. Provost , J. Anderton, B. Carterette, & P. Chandar. 
  

• Who’s Watching TV?  Jessica Clark, J.-F. Paiement, F. Provost. 

• Classification over bipartite graphs through projection. Marija Stankova, D. Martens, and F. Provost.

2020

• "Unsupervised Dimensionality Reduction vs. Supervised Regularization for Classification from Sparse Data.”  J. Clark & F. Provost (2019).  Data Mining and Knowledge Discovery 33(4):871–916.

• "Classification over bipartite graphs through projection.”  M. Stankova, S. Praet, D. Martens, and F. Provost. Machine Learning, forthcoming 2020.

• "Instance-level explanation algorithms SEDC, LIME, SHAP for behavioral and textual data: a counterfactual-oriented comparison.” Y. Ramon, D. Martens, F. Provost, & T. Evgeniou.  Forthcoming in Advances in Data Analysis and Classification 2020.

• "A Benchmarking Study of Classification Techniques for Behavioral Data.” S. De Cnudde, D. Martens, T. Evgeniou & F. Provost. International Journal of Data Science and Analytics 2/2020.

• "Unsupervised Dimensionality Reduction vs. Supervised Regularization for Classification from Sparse Data.”  J. Clark & F. Provost (2019).  Data Mining and Knowledge Discovery 33(4):871–916.

• "Deep Learning on Big, Sparse, Behavioral Data.”  S. De Cnudde, Y. Ramon, D. Martens & F. Provost (2019).  Big Data 7(4): 286-307.

2018

• “Data-Driven Investment Strategies for Peer-to-Peer Lending.” M. Cohen, C.D. Guetta, K. Jiao, F. Provost (2018).  Big Data 6(3), 191-213.
  

• "Wallenius Bayes.” E. Junqué de Fortuny, D. Martens, and F. Provost (2018). Machine Learning 107 (6): 1013–1037.

2017

•    “In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions.” E. Ascarza, S. Neslin, O. Netzer, Z. Anderson, P. Fader, S. Gupta, B. Hardie, A. Lemmens, B. Libai, D. Neal, F. Provost, R. Schrift (2017). Customer Needs and Solutions.  Also: Marketing Science Institute Working Paper Series 2017 Report No. 17-118.

       
  

• “Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.” D. Chen, S. Fraiberger, R. Moakler & F. Provost (2017).  Big Data 5(3): 197-212.

• What Managers Need to Know About Big Data. Foster Provost & Jim Euchner. Research Technology Management 60(3), 11-17, 2017.

• “Big Data, Data Science, and Civil Rights.”  Barocas, Bradley, Honavar, and Provost (2017).  Invited paper for the Computing Community Consortium of the Computing Research Association (CRA). http://cra.org/ccc/resources/ccc-led-whitepapers/

• Cost-Effective Quality Assurance in Crowd Labeling.  J. Wang, P. Ipeirotis, and F. Provost. Information Systems Research, forthcoming.

2016

• Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics.  Martens, D., F. Provost, J. Clark, E. Junqué de Fortuny.  MIS Quarterly, Vol. 40 No. 4, pp. 869-888/December 2016

• Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals. Daizhuo Chen, Samuel P. Fraiberger, Robert Moakler, Foster Provost.  ICML-2016 Workshop on Human Interpretability in Machine Learning (WHI 2016). 

• Explaining Classification Models Built on High-Dimensional Sparse Data. Julie Moeyersoms, Brian d'Alessandro, Foster Provost, David Martens.  ICML-2016 Workshop on Human Interpretability in Machine Learning (WHI 2016). 

2015

• Beat the Machine: Challenging Humans to Find a Predictive Model's ``Unknown Unknowns''.  J. Attenberg, P. Ipeirotis, F. Provost.  ACM Journal of Data and Information Quality 6(1) 2015.
• Evaluating and Optimizing Online Advertising: Forget the click, but there are good proxies.  B. Dalessandro, R. Hook, C. Perlich, F. Provost.  Big Data. Jun 2015: 3(2): 90-102.
• Prior version: NYU Working Paper No. CBA-12-02. (People’s Choice Award Runner-up at the 2012 Empirical Generalizations in Advertising Conference.)
• Finding Mobile Consumers with a Privacy-Friendly Geo-Similarity Network.  F. Provost, D. Martens and A. Murray.  Information Systems Research 26(2), 243-265, 2015.  Best Paper Award.  Also INFORMS President's Pick.

• Measuring causal impact of online actions via natural experiments: application to display advertising.  D. Hill, R. Moakler, A. Hubbard, V. Tsemekhman, F. Provost, K, Tsemekhman.  In the Proceedings of the Twenty-first ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015).

• Iteratively Refining SVMs using Priors.  E. Junque de Fortuny, T. Evgeniou, F. Provost, and D. Martens.  IEEE International Conference on Big Data (IEEE BigData 2015).

2014

• Explaining Data-driven Document Classifications.  Martens, D. and F. Provost.  MIS Quarterly 38(1), 73-99, 2014
• Repeated Labeling Using Multiple Noisy Labelers.   Ipeirotis, P., F. Provost, V. Sheng, J. Wang.  Data Mining and Knowledge Discovery 28(2), 402-441, 2014.

• Corporate Residence Fraud Detection.  D. Martens, et al.  In Proceedings of the Twentieth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014).

• Scalable Hands-Free Transfer Learning for Online Advertising.  B. Dalessandro, et al.  In Proceedings of the Twentieth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014).

• Machine learning for targeted display advertising: Transfer learning in action.  C. Perlich · B. Dalessandro · O. Stitelman · T. Raeder · F. Provost.  Machine Learning 95(1):103-127, 2014.  
• A Data Scientist’s Guide to Startups.  F. Provost, G. Webb, R. Bekkerman, O. Etzioni, U. Fayyad, C. Perlich.  Big Data 2(3):117-128, September 2014.
• Pleasing the advertising oracle.  Probabilistic prediction from sampled, aggregated ground truth.  M. Williams, C. Perlich, B. Dalessandro, F. Provost.  In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising (ADKDD 2014).  
• Causal impact of online advertisements using viewability as a method of treatment.  R. Moakler, et al.  Winter Conference on Business Intelligence, Feb. 2014.  (See Moakler et al. KDD 2015 above.)
  

• Data Science and its relation to Big Data and data-driven decision making.  F. Provost and T. Fawcett. Big Data 1(1), 2013.
• Predictive Modeling with Big Data: Is Bigger Really Better?  E. Junqué de Fortuny, D. Martens and F. Provost.  Big Data 1(4), 2013
• Information in Digital, Economic and Social Networks.  Sundararajan, A., F. Provost, G. Oestreicher-Singer, and S. Aral. Information Systems Research 24(4), 883-905, 2013.
• Scalable Supervised Dimensionality Reduction Using Clustering.  Raeder, T., C. Perlich, B. Dalessandro, O. Stitelman, and F. Provost.  In Proceedings of the Nineteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2013).
• Using Co-visitation Networks For Classifying Non-Intentional Traffic.  Stitelman, O., C. Perlich, B. Dalessandro, R. Hook, T. Raeder, and F. Provost.  In Proceedings of the Nineteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2013).
• Hyperlocal: Inferring Location of IP Addresses in Real-time Bid Requests for Mobile Ads (with L.T. Le, F. Provost, and L. Moores), Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN'13), Orlando, FL, November 2013

2012

• Bid Optimizing and Inventory Scoring in Targeted Online Advertising.  Perlich, C., B. Dalessandro, R. Hook, O. Stitelman, T. Raeder, and F. Provost. In Proceedings of the Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012).  Best Paper Award, Industry & Government Track.
• Design Principles of Massive, Robust Prediction Systems.  Raeder, T., O. Stitelman, B. Dalessandro, C. Perlich, and F. Provost.  In Proceedings of the Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012).

2011

• Online Active Inference and Learning. J. Attenberg and F. Provost. To appear in Proceedings of the Seventeenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  (KDD 2011),
• Beat the Machine: Challenging workers to find the unknown unknowns. J. Attenberg, P. Ipeirotis and F. Provost. In Proceedings of the 3rd Human Computation Workshop (HCOMP 2011).
• Selective Data Acquisition for Machine Learning. J. Attenberg, P. Melville, F. Provost, and M. Saar-Tsechansky. To appear in B. Krishnapuram, S. Yu, B. Rao (eds.), Cost-Sensitive Machine Learning, 2011.

2010

• Inactive Learning? Difficulties Employing Active Learning in Practice. J. Attenberg and F. Provost. SIGKDD Explorations 12(2), 2010.
• Why label when you can search? Alternatives to active learning for applying human resources to build classification models under extreme class imbalance. J. Attenberg and F. Provost. To appear in Proceedings of the Sixteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  (KDD 2010).
  • Prior working paper: Toward optimal allocation of human resources for active learning with application to safe on-line advertising. J. Attenberg & F. Provost. Working paper CeDER-09-07. Center for Digital Economy Research, Stern School of Business, New York University. December 2009.
• Active inference and learning for classifying streams. J. Attenberg and F. Provost. In ICML-2010 Workshop on Budgeted Learning.
• Guided feature labeling for budget-sensitive learning under extreme class imbalance. J. Attenberg, P. Melville and F. Provost. In ICML-2010 Workshop on Budgeted Learning.
• A Unified Approach to Active Dual Supervision. J. Attenberg, P. Melville and F. Provost. To appear in Proceedings of the European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECML PKDD 2010).
• Quality Management on Amazon Mechanical Turk, P. Ipeirotis, F. Provost, and J. Wang. Proceedings of the Second Human Computation Workshop (KDD-HCOMP 2010).

2009

• Audience Selection for On-line Brand Advertising: Privacy-friendly Social Network Targeting. Provost, F., B. Dalessandro, R. Hook, X. Zhang, and A. Murray.. To appear in Proceedings of the Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  (KDD 2009).
• Toward optimal allocation of human resources for active learning with application to safe on-line advertising. J. Attenberg & F. Provost. Working paper CeDER-09-07. Center for Digital Economy Research, Stern School of Business, New York University. December 2009.
• Social Network Collaborative Filtering. Zheng, R., D. Wilkinson, and F. Provost. Working paper CeDER-8-08. Center for Digital Economy Research, Stern School of Business, New York University. 2008.

2008

• Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers Sheng, S., F. Provost and P. Ipeirotis. In Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  (KDD 2008). Best Paper Award Runner-up.
  • Prior version: Noisy Multi-labeling for Data Mining. CeDER Working Paper CeDER-08-01, Stern School of Business, New York University. March 2008.
• Active Feature-value Acquisition. Saar-Tsechansky, M., P. Melville and F. Provost. Management Science 55(4) 664-684.
  • Prior version: Active Feature-value Acquisition. McCombs Research Paper Series No. IROM-08-06. Latest Draft: Sept. 2007.

2007

• Social Network Collaborative Filtering: Preliminary Results . R. Zheng, F. Provost and A. Ghose. To appear in the Proceedings of the Sixth Workshop on eBusiness (WEB2007), December 2007.
• Learning and Inference in Massive Social Networks. S. Hill, F. Provost and C. Volinsky (2007). The 5th International Workshop on Mining and Learning with Graphs, August 2007.
• Handling Missing Features when Applying Classification Models. M. Saar-Tsechansky and F. Provost. Journal of Machine Learning Research 8(July):1625-1657 .
• Classification in Networked Data: A toolkit and a univariate case study. S. Macskassy and F. Provost Journal of Machine Learning Research 8(May):935--983, 2007.
• Decision-centric Active Learning of Binary-Outcome Models M. Saar-Tsechansky and F. Provost. Information Systems Research 18(1), March 2007, pp. 4-22. (Publisher's version)
• Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. F. Provost, P. Melville, and M. Saar-Tsechansky. Proceedings of the Ninth International Conference on Electronic Commerce, August 2007.
• Modeling Complex Networks for (Electronic) Commerce. F. Provost and A. Sundararajan. Tutorial given at the 2007 ACM Conference on Electronic Commerce (EC07).

2006

• Distribution-based Aggregation for Relational Learning from Identifier Attributes. C. Perlich and F. Provost. Machine Learning 62 (1/2) 65-105, 2006. (Publisher's version)
• Network-based Marketing: Identifying likely adopters via consumer networks. S. Hill, F. Provost, and C. Volinsky. Statistical Science 21 (2) 256–276, 2006. (Publisher's version)

2005

• An Intelligent Assistant for the Knowledge Discovery Process: An Ontology-based Approach A. Bernstein, F. Provost and S. Hill. IEEE Transactions on Knowledge and Data Engineering 17(4), pp. 503-518, 2005. (PDF) (Publisher's version)
• ROC Confidence Bands: An Empirical Evaluation. S. Macskassy, F. Provost, and S. Rosset. In Proceedings of the 22nd International Conference on Machine Learning (ICML-2005). [Also appears in the ICML-2005 Workshop on ROC Analysis in Machine Learning (ROCML-2005).]
• Suspicion scoring based on guilt-by-association, collective inference, and focused data access. S. Macskassy and F. Provost. In Proceedings of the 2005 International Conference on Intelligence Analysis (IA '05).
• An Expected Utility Approach to Active Feature-value Acquisition. P. Melville, M. Saar-Tsechansky, F. Provost, and R. Mooney. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM-2005), pp. 483-486. Also appeared in Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, Chicago, IL, August 2005.
• Suspicion scoring based on guilt-by-association, collective inference, and focused data access. S. Macskassy and F. Provost. In Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), 2005. [This is a followup paper to the IA paper above, with new results but considerable overlap, and unfortunately with the same title.]
• NetKit-SRL: A Network Learning Toolkit and its use for classification of networked data. S. Macskassy and F. Provost. In Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), 2005.
• Pointwise ROC Confidence Bounds: An Empirical Evaluation. S. Macskassy, F. Provost, and S. Rosset. In the ICML-2005 Workshop on ROC Analysis in Machine Learning (ROCML-2005).
• Aggregation for Predictive Modeling with Relational Data. C. Perlich and F. Provost. Encyclopedia of Data Warehousing and Mining, 2005
• Toward a Justification of Meta-learning: Is the No Free Lunch Theorem a Show-stopper? C. Giraud-Carrier and F. Provost. In the ICML-2005 Workshop on Meta-Learning.

2004

• Active Sampling for Class Probability Estimation and Ranking. M Saar-Tsechansky and F. Provost. Machine Learning 54:2 2004, 153-178. (Publisher's version)
• Active Feature-Value Acquisition for Classifier Induction. P. Melville, M. Saar-Tsechansky, F. Provost, and R. Mooney.  In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM-2004).
• Confidence Bands for ROC Curves: Methods and an Empirical Study. S. Macskassy and F. Provost.   In Proceedings of the First Workshop on ROC Analysis in AI.  August 2004.
• Knowledge Discovery Using Concept-Class Taxonomies. V. Kolluri, F. Provost, B. Buchanan, and D. Metzler. In AI 2004: Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence. Lecture Notes in Computer Science, Springer-Verlag Heidelberg .
• The Gift of Gab: Evidence TelE-Commerce Firms can Profit from Viral Marketing. S. Hill, F. Provost and C. Volinsky. First Interdisciplinary Symposium between Information Systems, Statistics and Related Fields. Decision and Information Technologies Department, Robert H. Smith School of Business, Univ. of Maryland. May 2005

• The Myth of the Double-blind Review? Author Identification using only Citations. S. Hill and F. Provost. In SIGKDD Explorations 5(2), 2003, 179-184.
• Predicting citation rates for physics papers: Constructing features for an ordered probit model. C. Perlich, F. Provost, and S. Macskassy. In SIGKDD Explorations 5(2), 2003, 154-155.
• Aggregation-based Feature Invention and Relational Concept Classes. C. Perlich and F. Provost.  In Proceedings of the Ninth SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003).
• Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction. G. Weiss and F. Provost.  Journal of Artificial Intelligence Research 19 (2003) 315-354.
  Prior versions:
  • Weiss, G. and F. Provost. "The Effect of Class Distribution on Classifier Learning: An Empirical Study"  Technical Report ML-TR-44, Department of Computer Science, Rutgers University. (PDF)
  • Weiss, G. and F. Provost. "The Effect of Class Distribution on Classifier Learning."  Technical Report ML-TR-43, Department of Computer Science, Rutgers University, January 2001. (PDF) [Preliminary version of ML-TR-44]
• Tree Induction vs. Logistic Regression: A Learning-curve Analysis. C. Perlich, F. Provost, and J. Simonoff.  Journal of Machine Learning Research 4 (2003) 211-255.
• Preliminary version: CeDER Working Paper #IS-01-02, Stern School of Business, New York University, NY, NY 10012.  Fall 2001.  (preliminary version: PDF,PS)
• Tree Induction for Probability-based Rankings.  F. Provost. and P. Domingos. Machine Learning 52:3. (PDF) (Publisher's version)
• Invited comment on Bolton and Hand’s “Statistical Fraud Detection.” F. Provost. Statistical Science.
• The Relational Vector-space Model and Industry Classification.  A. Bernstein, S. Clearwater, and  F. Provost. Proceedings of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data.
• A Simple Relational Classifier. S. Macskassy and F. Provost. Proceedings of the KDD-2003 Workshop on Multirelational Data Mining.
• Relational Learning Problems and Simple Models.  F. Provost, C. Perlich, and S. Macskassy.  Proceedings of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data.
• Aggregation and Concept Complexity in Relational Learning. C. Perlich and F. Provost. Proceedings of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data.

• Perlich, C. and F. Provost.  "A Modular Approach to Relational Data Mining."  American Conference on Information Systems (AMCIS) 2002.
• Bernstein, A., S. Clearwater, S. Hill, C. Perlich, and F. Provost.  “Discovering Knowledge from Relational Data Extracted from Business News.”  In Proceedings of the KDD-2002 Workshop on Multi-Relational Data Mining, 2002.
• Provost, F. and V. Kolluri, "Scalability." In W. Kloesgen and J. Zytkow (eds.), Handbook of Knowledge Discovery and Data Mining.
• Danyluk, A. and F. Provost, "Telecommunications Network Diagnosis." In W. Kloesgen and J. Zytkow (eds.), Handbook of Knowledge Discovery and Data Mining. (PDF)
• Fawcett, T. and F. Provost, "Data Mining for Fraud Detection." In W. Kloesgen and J. Zytkow (eds.), Handbook of Knowledge Discovery and Data Mining.

• Saar-Tsechansky, M. and F. Provost, "Active Learning for Class Probability Estimation and Ranking." In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01). [See also extended version (to appear in Machine Learning, see above)]
• Macskassy, S., H. Hirsh, F. Provost, R. Sankaranarayanan, V. Dhar. “Intelligent Information Triage.”  In Proceedings of SIGIR-2001.
• Bernstein, A. and F. Provost.  "An Intelligent Assistant for the Knowledge Discovery Process."  In Proceedings of IJCAI-01 Workshop on Wrappers for Performance Enhancement in KDD. (CeDER Working Paper #IS-01-01, Stern School of Business, New York University, January 2001.)
• Provost, F. and T. Fawcett, "Robust Classification for Imprecise Environments." Machine Learning 42, 203-231, 2001. (PDF) (Publisher's version)
• Kohavi, R. and F. Provost (eds.), Special Issue on Applications of Data Mining to Electronic Commerce, Data Mining and Knowledge Discovery 5, (1/2) 2001.
• Kohavi, R. and F. Provost, Applications of Data Mining to Electronic Commerce (introductory article), Data Mining and Knowledge Discovery 5, (1/2) 2001.

• Dhar, V., D. Chou, and F. Provost, "Discovering Interesting Patterns for Investment Decision Making with GLOWER -- A Genetic Learner Overlaid With Entropy Reduction," Data Mining and Knowledge Discovery 4(4) 2000.
• Provost, F.  "Learning with Imbalanced Data Sets 101." Invited paper for the AAAI'2000 Workshop on Imbalanced Data Sets.
• Provost, F., D. Jensen and T. Oates, "Progressive Sampling."  In H. Liu and H. Motoda (eds.), Instance Selection and Construction, A Data Mining Perspective.
• Provost, F., "Distributed Data Mining: Scaling up and beyond." In H. Kargupta and P. Chan (eds.), Advances in Distributed Data Mining, San Francisco, CA: Morgan Kaufmann.
• Provost, F., and P. Domingos. "Well-trained PETs: Improving Probability Estimation Trees" CeDER Working Paper #IS-00-04, Stern School of Business, New York University, NY, NY 10012 (PDF) [Journal version is much improved.]

• Provost, F. and V. Kolluri, "A Survey of Methods for Scaling Up Inductive Algorithms." Data Mining and Knowledge Discovery 3 (1999). (PDF)
• Provost, F. and A. Danyluk, "Problem Definition, Data Cleaning and Evaluation: A Classifier Learning Case Study." Informatica 23 (1999). (PDF)
• Provost, F., D. Jensen and T. Oates, "Efficient Progressive Sampling." Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99).
• T. Fawcett and F. Provost, "Activity Monitoring: Noticing Interesting Changes in Behavior." Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99).
• Danyluk, A., T. Fawcett, and F. Provost, "AI Approaches to Time-series Problems." Workshop report in AI Magazine, 1999.
• Provost, F. and D. Jensen, "Evaluating Machine Learning, Knowledge Discovery, and Data Mining." Tutorial presented at the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99) and at the Sixteenth National Conference on Artificial Intelligence (AAAI-99). (abstract | links)
• Provost, F., J. Aronis, and B. Buchanan. "Rule-space search for knowledge-based discovery."  CIIO Working Paper #IS 99-012, Stern School of Business, New York University, NY, NY 10012 (PDF)

• Provost, F. and R. Kohavi, "On Applied Research in Machine Learning." Guest editorial in Machine Learning 30 (2/3) 1998. (postscript)
• Kohavi, R. and F. Provost (guest eds.), Special issue on "Applications of Machine Learning and the Knowledge Discovery Process." Machine Learning 30 (2/3) 1998. (table of contents)
• Provost, F. and T. Fawcett, "Robust Classification Systems for Imprecise Environments." In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98).
• Provost, F., T. Fawcett, and R. Kohavi "The Case Against Accuracy Estimation for Comparing Classifiers." In Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98).
• Fawcett, T., I. Haimowitz, F. Provost, and S. Stolfo, "AI Approaches to Fraud Detection and Risk Management." Workshop report in AI Magazine, 1998.
• Provost, F. and D. Jensen, "Evaluating Data Mining and the Knowledge Discovered." Tutorial presented at the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98).
• Fawcett, T. and F. Provost, "Automatic Design of Fraud Detection Systems" U.S. Patent #5,790,645.

• Fawcett, T. and F. Provost, "Adaptive Fraud Detection." Data Mining and Knowledge Discovery 1 (1997).
• Provost, F. and T. Fawcett, "Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions." In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97). Best Paper Award Winner.
• Provost, F. and V. Kolluri, "Scaling Up Inductive Algorithms: An Overview." In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97).
• Aronis, J. and F. Provost, "Increasing the Efficiency of Inductive Learning with Breadth-first Marker Propagation." In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97).
• Aronis, J., V. Kolluri, F. Provost, and B. Buchanan, "The WoRLD: Knowledge Discovery from Multiple Distributed Databases." In Proc. of the Florida Artificial Intelligence Research Symposium (FLAIRS-97).

• Provost, F. and J. Aronis, "Scaling Up Machine Learning with Massive Parallelism." Machine Learning 23 (1996).
• Provost, F. and D. Hennessy, "Scaling Up: Distributed Machine Learning with Cooperation." In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96).
• Fawcett, T. and F. Provost, "Combining Data Mining and Machine Learning for Effective User Profiling." In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96).
• Aronis, J., F. Provost, and B. Buchanan, "Exploiting Background Knowledge in Automated Discovery." In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96).

• Provost, F. and B. Buchanan, "Inductive Policy: The Pragmatics of Bias Selection." Machine Learning 20 (1995).
• Krenzelok, E. and F. Provost, "The Ten Most Common Plant Exposures Reported to Poison Information Centers in the United States." Journal of Natural Toxins (1995).
• Provost, F. and A. Danyluk, "Learning from Bad Data." In Proceedings of the ML-95 Workshop on Applying Machine Learning in Practice, 1995.
• Krenzelok, E., F. Provost, T. Jacobsen, J. Aronis, B. Buchanan, "Assessing Patient Referral Patterns to a Health Care Facility in Plant Exposure Patients Using Computer Artificial Intelligence." European Association of Poison Centres and Clinical Toxicologists Scientific Meeting. May 18-20, 1995, Krakow, Poland.
• Krenzelok, E., F. Provost, T. Jacobsen, J. Aronis, B. Buchanan, "Poinsettia (Euphorbia pulcherrima) Exposures Have Good Outcomes...Just As We Thought." European Association of Poison Centres and Clinical Toxicologists Scientific Meeting, 1995.

• Provost, F. and D. Hennessy, "Distributed Machine Learning: Scaling up with Coarse-grained Parallelism." In Proc of the Second International Conference on Intelligent Systems for Molecular Biology (ISMB-94).
• Aronis, J. and F. Provost, "Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning" In Proceedings of the AAAI-94 Workshop on Knowledge Discovery in Databases, (KDD-94).
• Provost, F., "Goal-Directed Inductive Learning: Trading Off Accuracy for Reduced Error Cost." In Proceedings of the AAAI Spring Symposium on Goal-Directed Learning, 1994.

• Provost, F., "Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias." In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93).
• Danyluk, A. and F. Provost, "Small Disjuncts in Action: Learning to Diagnose Errors in the Telephone Network Local Loop." In Proceedings of the Tenth International Conference on Machine Learning (ICML-93).
• Danyluk, A. and F. Provost, "Adaptive Expert Systems: Applying Machine Learning to NYNEX MAX." In Proceedings of the AAAI-93 Workshop: AI in Service and Support--Bridging the Gap between Research and Applications, 1993.

• Provost, F. and R. Melhem, "A Distributed Algorithm for Embedding Trees in Hypercubes with Modifications for Run-Time Fault Tolerance." Journal of Parallel and Distributed Computing 14 (1992).
• Provost, F. and B. Buchanan, "Inductive Policy." In Proceedings of the Tenth National Conference on Artificial Intelligence(AAAI-92).
• Provost, F., "ClimBS: Searching the Bias Space." In Proceedings of the Fourth International IEEE Conference on Tools with Artificial Intelligence(TAI-92).
• Provost, F., "A Baseline Taxonomy of Bias Adjustment Policies." In Proceedings of the ML-92 Workshop on Biases in Learning, 1992.
• Provost, F. and B. Buchanan, "Inductive Strengthening: The effects of a simple heuristic for restricting hypothesis space search." In K.P. Jantke (ed.), Analogical and Inductive Inference (Lecture Notes in Artificial Intelligence 642). Springer-Verlag, 1992.
• Clearwater, S., W. Cleland, F. Provost, E. Stern and Z. Zhang, "A Real-Time Expert System for Experimental High Energy/Nuclear Physics." In D. Perrett-Gallix and W. Wojcik (eds.), New Computing Techniques in Physics Research. Paris: Centre National de la Recherche Scientific, 1990.

• Provost, F. and R. Melhem, "Embedding Rings in Hypercubes for Run-Time Fault Tolerance." In Proceedings of the Fourth ISMM/IASTED Intl. Conference on Parallel and Distributed Computing and Systems, 1991.

• Clearwater, S., W. Cleland, F. Provost, E. Stern and Z. Zhang, "A Real-Time Expert System for Trigger Logic Monitoring." Nuclear Instruments and Methods in Physics Research A293 (1990).
• Clearwater, S. and F. Provost, "RL4: A Tool for Knowledge-Based Induction." In Proceedings of the Second International IEEE Conference on Tools for Artificial Intelligence (TAI-90).

• Provost F. and R. Melhem, "Distributed Fault Tolerant Embedding of Trees and Rings in Hypercubes." In I. Koren (ed.) Defect and Fault Tolerance in VLSI systems Volume 1. New York, NY: Plenum Press, 1989

Teaching

In recent years, Professor Provost has taught:

• Introduction to Data Science
• Practical Data Science
• Data Mining for Business Analytics
• Networks, Crowds & Markets
• Data Science Research Seminar