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ORCIDFoster J. Provost
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- affiliation: New York University, USA
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2020 – today
- 2024
[c56]Connor Douglas, Foster J. Provost, Arun Sundarajan:
Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete? ICIS 2024- [i13]Carlos Fernández-Loría, Yanfang Hou, Foster J. Provost, Jennifer Hill:
Causal Fine-Tuning and Effect Calibration of Non-Causal Predictive Models. CoRR abs/2406.09567 (2024) - [i12]Connor Douglas, Foster J. Provost, Arun Sundararajan:
Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete? CoRR abs/2411.16574 (2024) - 2023
- [j50]Carlos Fernández-Loría
, Foster J. Provost, Jesse Anderton, Benjamin A. Carterette, Praveen Chandar:
A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation. Inf. Syst. Res. 34(2): 786-803 (2023) - [j49]Jessica Clark, Jean-François Paiement, Foster J. Provost:
Who's Watching TV? Inf. Syst. Res. 34(4): 1622-1640 (2023) - [i11]Sofie Goethals, Sandra C. Matz, Foster J. Provost, Yanou Ramon, David Martens:
The Impact of Cloaking Digital Footprints on User Privacy and Personalization. CoRR abs/2312.15000 (2023) - 2022
- [j48]Carlos Fernández-Loría, Foster J. Provost:
Causal Classification: Treatment Effect Estimation vs. Outcome Prediction. J. Mach. Learn. Res. 23: 59:1-59:35 (2022) - 2021
- [j47]Marija Stankova, Stiene Praet, David Martens, Foster J. Provost:
Node classification over bipartite graphs through projection. Mach. Learn. 110(1): 37-87 (2021) - [i10]Carlos Fernández-Loría, Foster J. Provost:
Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters. CoRR abs/2104.04103 (2021) - 2020
- [j46]Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou:
A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C. Adv. Data Anal. Classif. 14(4): 801-819 (2020) - [j45]Sofie De Cnudde, David Martens, Theodoros Evgeniou, Foster J. Provost:
A benchmarking study of classification techniques for behavioral data. Int. J. Data Sci. Anal. 9(2): 131-173 (2020) - [j44]Foster J. Provost:
In memory of Tom Fawcett. Mach. Learn. 109(11): 1987-1992 (2020) - [c55]Carlos Fernández-Loría, Foster J. Provost:
Combining Observational and Experimental Data to Improve Large-Scale Decision-Making. ICIS 2020 - [c54]Ron Bekkerman, Vanja Josifovski, Foster J. Provost:
Data Science for the Real Estate Industry. KDD 2020: 3559-3560 - [i9]Carlos Fernandez, Foster J. Provost, Xintian Han:
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach. CoRR abs/2001.07417 (2020) - [i8]Carlos Fernandez, Foster J. Provost, Jesse Anderton, Benjamin A. Carterette, Praveen Chandar:
Methods for Individual Treatment Assignment: An Application and Comparison for Playlist Generation. CoRR abs/2004.11532 (2020)
2010 – 2019
- 2019
- [j43]Sofie De Cnudde, Yanou Ramon, David Martens, Foster J. Provost:
Deep Learning on Big, Sparse, Behavioral Data. Big Data 7(4): 286-307 (2019) - [j42]Jessica Clark, Foster J. Provost:
Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data. Data Min. Knowl. Discov. 33(4): 871-916 (2019) - [c53]Carlos Fernandez, Foster J. Provost, Xintian Han:
Counterfactual Explanations for Data-Driven Decisions. ICIS 2019 - [i7]Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou:
Counterfactual Explanation Algorithms for Behavioral and Textual Data. CoRR abs/1912.01819 (2019) - 2018
- [j41]Maxime C. Cohen, C. Daniel Guetta, Kevin Jiao, Foster J. Provost:
Data-Driven Investment Strategies for Peer-to-Peer Lending: A Case Study for Teaching Data Science. Big Data 6(3): 191-213 (2018) - [j40]Enric Junqué de Fortuny, David Martens, Foster J. Provost:
Wallenius Bayes. Mach. Learn. 107(6): 1013-1037 (2018) - [c52]Foster J. Provost, James Hodson, Jeannette M. Wing, Qiang Yang, Jennifer Neville:
Societal Impact of Data Science and Artificial Intelligence. KDD 2018: 2872-2873 - [e4]Francesco Bonchi, Foster J. Provost, Tina Eliassi-Rad, Wei Wang, Ciro Cattuto, Rayid Ghani:
5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018, Turin, Italy, October 1-3, 2018. IEEE 2018, ISBN 978-1-5386-5090-5 [contents] - 2017
- [j39]Daizhuo Chen, Samuel P. Fraiberger, Robert Moakler, Foster J. Provost:
Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals. Big Data 5(3): 197-212 (2017) - [j38]Jing Wang, Panagiotis G. Ipeirotis, Foster J. Provost:
Cost-Effective Quality Assurance in Crowd Labeling. Inf. Syst. Res. 28(1): 137-158 (2017) - [i6]Solon Barocas, Elizabeth Bradley, Vasant G. Honavar, Foster J. Provost:
Big Data, Data Science, and Civil Rights. CoRR abs/1706.03102 (2017) - 2016
- [j37]David Martens, Foster J. Provost, Jessica Clark, Enric Junqué de Fortuny:
Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics. MIS Q. 40(4): 869-888 (2016) - [c51]Foster J. Provost:
The Predictive Power of Massive Data about our Fine-Grained Behavior. WSDM 2016: 471-472 - [i5]Daizhuo Chen, Samuel P. Fraiberger, Robert Moakler, Foster J. Provost:
Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals. CoRR abs/1606.08063 (2016) - [i4]Julie Moeyersoms, Brian Dalessandro, Foster J. Provost, David Martens:
Explaining Classification Models Built on High-Dimensional Sparse Data. CoRR abs/1607.06280 (2016) - 2015
- [j36]Brian Dalessandro, Rod Hook, Claudia Perlich, Foster J. Provost:
Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies. Big Data 3(2): 90-102 (2015) - [j35]Foster J. Provost, David Martens, Alan Murray:
Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design. Inf. Syst. Res. 26(2): 243-265 (2015) - [j34]Joshua Attenberg, Panos Ipeirotis, Foster J. Provost:
Beat the Machine: Challenging Humans to Find a Predictive Model's "Unknown Unknowns". ACM J. Data Inf. Qual. 6(1): 1:1-1:17 (2015) - [c50]Enric Junqué de Fortuny, Theodoros Evgeniou, David Martens, Foster J. Provost:
Iteratively refining SVMs using priors. IEEE BigData 2015: 46-52 - [c49]Daniel N. Hill, Robert Moakler, Alan E. Hubbard, Vadim Tsemekhman, Foster J. Provost, Kiril Tsemekhman:
Measuring Causal Impact of Online Actions via Natural Experiments: Application to Display Advertising. KDD 2015: 1839-1847 - 2014
- [j33]Foster J. Provost, Tom Fawcett:
Authors' Response to Gong's, "Comment on Data Science and its Relationship to Big Data and Data-Driven Decision Making". Big Data 2(1): 1 (2014) - [j32]Foster J. Provost:
ACM SIGKDD 2014 to be Held August 24-27 in Manhattan. Big Data 2(2): 71-72 (2014) - [j31]Foster J. Provost, Geoffrey I. Webb, Ron Bekkerman, Oren Etzioni, Usama M. Fayyad, Claudia Perlich:
A Data Scientist's Guide to Start-Ups. Big Data 2(3): 117-128 (2014) - [j30]Panagiotis G. Ipeirotis, Foster J. Provost, Victor S. Sheng, Jing Wang:
Repeated labeling using multiple noisy labelers. Data Min. Knowl. Discov. 28(2): 402-441 (2014) - [j29]David Martens, Foster J. Provost:
Explaining Data-Driven Document Classifications. MIS Q. 38(1): 73-99 (2014) - [j28]Claudia Perlich, Brian Dalessandro, Troy Raeder, Ori Stitelman, Foster J. Provost:
Machine learning for targeted display advertising: transfer learning in action. Mach. Learn. 95(1): 103-127 (2014) - [c48]Melinda Han Williams, Claudia Perlich, Brian Dalessandro, Foster J. Provost:
Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground truth. ADKDD@KDD 2014: 3:1-3:9 - [c47]Brian Dalessandro, Daizhuo Chen, Troy Raeder, Claudia Perlich, Melinda Han Williams, Foster J. Provost:
Scalable hands-free transfer learning for online advertising. KDD 2014: 1573-1582 - [c46]Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster J. Provost, David Martens:
Corporate residence fraud detection. KDD 2014: 1650-1659 - 2013
- [j27]Foster J. Provost, Tom Fawcett:
Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data 1(1): 51-59 (2013) - [j26]Enric Junqué de Fortuny, David Martens, Foster J. Provost:
Predictive Modeling With Big Data: Is Bigger Really Better? Big Data 1(4): 215-226 (2013) - [j25]Arun Sundararajan, Foster J. Provost, Gal Oestreicher-Singer, Sinan Aral:
Research Commentary - Information in Digital, Economic, and Social Networks. Inf. Syst. Res. 24(4): 883-905 (2013) - [c45]Long T. Le, Tina Eliassi-Rad, Foster J. Provost, Lauren Moores:
Hyperlocal: inferring location of IP addresses in real-time bid requests for mobile ads. LBSN 2013: 24-33 - [c44]Troy Raeder, Claudia Perlich, Brian Dalessandro, Ori Stitelman, Foster J. Provost:
Scalable supervised dimensionality reduction using clustering. KDD 2013: 1213-1221 - [c43]Ori Stitelman, Claudia Perlich, Brian Dalessandro, Rod Hook, Troy Raeder, Foster J. Provost:
Using co-visitation networks for detecting large scale online display advertising exchange fraud. KDD 2013: 1240-1248 - [c42]Foster J. Provost, Geoffrey I. Webb:
Panel: a data scientist's guide to making money from start-ups. KDD 2013: 1445 - 2012
- [c41]Brian Dalessandro, Claudia Perlich, Ori Stitelman, Foster J. Provost:
Causally motivated attribution for online advertising. AdKDD@KDD 2012: 7:1-7:9 - [c40]Claudia Perlich, Brian Dalessandro, Rod Hook, Ori Stitelman, Troy Raeder, Foster J. Provost:
Bid optimizing and inventory scoring in targeted online advertising. KDD 2012: 804-812 - [c39]Troy Raeder, Ori Stitelman, Brian Dalessandro, Claudia Perlich, Foster J. Provost:
Design principles of massive, robust prediction systems. KDD 2012: 1357-1365 - 2011
- [c38]Josh Attenberg, Panagiotis G. Ipeirotis, Foster J. Provost:
Beat the Machine: Challenging Workers to Find the Unknown Unknowns. Human Computation 2011 - [c37]Josh Attenberg, Foster J. Provost:
Online active inference and learning. KDD 2011: 186-194 - [i3]Foster J. Provost, Gary M. Weiss:
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction. CoRR abs/1106.4557 (2011) - 2010
- [j24]Josh Attenberg, Foster J. Provost:
Inactive learning?: difficulties employing active learning in practice. SIGKDD Explor. 12(2): 36-41 (2010) - [c36]Panagiotis G. Ipeirotis, Foster J. Provost, Jing Wang:
Quality management on Amazon Mechanical Turk. HCOMP@KDD 2010: 64-67 - [c35]Josh Attenberg, Foster J. Provost:
Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance. KDD 2010: 423-432 - [c34]Josh Attenberg, Prem Melville, Foster J. Provost:
A Unified Approach to Active Dual Supervision for Labeling Features and Examples. ECML/PKDD (1) 2010: 40-55 - [e3]Prem Melville, Jure Leskovec, Foster J. Provost:
Proceedings of the First Workshop on Social Media Analytics, SOMA@KDD 2010, Washington, DC, USA, July 25, 2010. ACM 2010, ISBN 978-1-4503-0217-3 [contents]
2000 – 2009
- 2009
- [j23]Maytal Saar-Tsechansky, Prem Melville, Foster J. Provost:
Active Feature-Value Acquisition. Manag. Sci. 55(4): 664-684 (2009) - [c33]Foster J. Provost:
Brand advertising, on-line audiences, and social media: invited talk. KDD Workshop on Data Mining and Audience Intelligence for Advertising 2009 - [c32]Foster J. Provost, Brian Dalessandro, Rod Hook, Xiaohan Zhang, Alan Murray:
Audience selection for on-line brand advertising: privacy-friendly social network targeting. KDD 2009: 707-716 - [e2]Paul N. Bennett, Raman Chandrasekar, Max Chickering, Panagiotis G. Ipeirotis, Edith Law, Anton Mityagin, Foster J. Provost, Luis von Ahn:
Proceedings of the ACM SIGKDD Workshop on Human Computation, Paris, France, June 28, 2009. ACM 2009, ISBN 978-1-60558-672-4 [contents] - 2008
- [c31]Victor S. Sheng, Foster J. Provost, Panagiotis G. Ipeirotis:
Get another label? improving data quality and data mining using multiple, noisy labelers. KDD 2008: 614-622 - 2007
- [j22]Maytal Saar-Tsechansky, Foster J. Provost:
Decision-Centric Active Learning of Binary-Outcome Models. Inf. Syst. Res. 18(1): 4-22 (2007) - [j21]Sofus A. Macskassy, Foster J. Provost:
Classification in Networked Data: A Toolkit and a Univariate Case Study. J. Mach. Learn. Res. 8: 935-983 (2007) - [j20]Maytal Saar-Tsechansky, Foster J. Provost:
Handling Missing Values when Applying Classification Models. J. Mach. Learn. Res. 8: 1623-1657 (2007) - [c30]Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky:
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. ICEC 2007: 389-398 - [c29]Shawndra Hill, Foster J. Provost, Chris Volinsky:
Learning and Inference in Massive Social Networks. MLG 2007 - [c28]Foster J. Provost, Arun Sundararajan:
Modeling complex networks for electronic commerce. EC 2007: 368 - 2006
- [j19]Claudia Perlich, Foster J. Provost:
Distribution-based aggregation for relational learning with identifier attributes. Mach. Learn. 62(1-2): 65-105 (2006) - [c27]Sofus Attila Macskassy, Foster J. Provost:
A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring. SNA@ICML 2006: 172-175 - 2005
- [j18]Abraham Bernstein, Foster J. Provost, Shawndra Hill:
Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification. IEEE Trans. Knowl. Data Eng. 17(4): 503-518 (2005) - [c26]Prem Melville, Foster J. Provost, Raymond J. Mooney:
An Expected Utility Approach to Active Feature-Value Acquisition. ICDM 2005: 745-748 - [c25]Sofus A. Macskassy, Foster J. Provost, Saharon Rosset:
ROC confidence bands: an empirical evaluation. ICML 2005: 537-544 - 2004
- [j17]Maytal Saar-Tsechansky, Foster J. Provost:
Active Sampling for Class Probability Estimation and Ranking. Mach. Learn. 54(2): 153-178 (2004) - [c24]Venkateswarlu Kolluri, Foster J. Provost, Bruce G. Buchanan, Douglas Metzler:
Knowledge Discovery Using Concept-Class Taxonomies. Australian Conference on Artificial Intelligence 2004: 450-461 - [c23]Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney:
Active Feature-Value Acquisition for Classifier Induction. ICDM 2004: 483-486 - [c22]Sofus A. Macskassy, Foster J. Provost:
Confidence Bands for ROC Curves: Methods and an Empirical Study. ROCAI 2004: 61-70 - 2003
- [j16]Gary M. Weiss, Foster J. Provost:
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction. J. Artif. Intell. Res. 19: 315-354 (2003) - [j15]Claudia Perlich, Foster J. Provost, Jeffrey S. Simonoff:
Tree Induction vs. Logistic Regression: A Learning-Curve Analysis. J. Mach. Learn. Res. 4: 211-255 (2003) - [j14]Foster J. Provost, Pedro M. Domingos:
Tree Induction for Probability-Based Ranking. Mach. Learn. 52(3): 199-215 (2003) - [j13]Claudia Perlich, Foster J. Provost, Sofus A. Macskassy:
Predicting citation rates for physics papers: constructing features for an ordered probit model. SIGKDD Explor. 5(2): 154-155 (2003) - [j12]Shawndra Hill, Foster J. Provost:
The myth of the double-blind review?: author identification using only citations. SIGKDD Explor. 5(2): 179-184 (2003) - [c21]Claudia Perlich, Foster J. Provost:
Aggregation-based feature invention and relational concept classes. KDD 2003: 167-176 - 2001
- [j11]Ron Kohavi, Foster J. Provost:
Applications of Data Mining to Electronic Commerce. Data Min. Knowl. Discov. 5(1/2): 5-10 (2001) - [j10]Foster J. Provost, Tom Fawcett:
Robust Classification for Imprecise Environments. Mach. Learn. 42(3): 203-231 (2001) - [c20]Maytal Saar-Tsechansky, Foster J. Provost:
Active Learning for Class Probability Estimation and Ranking. IJCAI 2001: 911-920 - [c19]Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar:
Intelligent Information Triage. SIGIR 2001: 318-326 - [e1]Doheon Lee, Mario Schkolnick, Foster J. Provost, Ramakrishnan Srikant:
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, San Francisco, CA, USA, August 26-29, 2001. ACM 2001, ISBN 1-58113-391-X [contents] - 2000
- [j9]Vasant Dhar, Dashin Chou, Foster J. Provost:
Discovering Interesting Patterns for Investment Decision Making with GLOWER - A Genetic Learner Overlaid with Entropy Reduction. Data Min. Knowl. Discov. 4(4): 251-280 (2000) - [i2]Foster J. Provost, Tom Fawcett:
Robust Classification for Imprecise Environments. CoRR cs.LG/0009007 (2000) - [i1]Ron Kohavi, Foster J. Provost:
Applications of Data Mining to Electronic Commerce. CoRR cs.LG/0010006 (2000)
1990 – 1999
- 1999
- [j8]Foster J. Provost, Venkateswarlu Kolluri:
A Survey of Methods for Scaling Up Inductive Algorithms. Data Min. Knowl. Discov. 3(2): 131-169 (1999) - [j7]Foster J. Provost, Andrea Pohoreckyj Danyluk:
Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study. Informatica (Slovenia) 23(1) (1999) - [c18]Foster J. Provost, David D. Jensen, Tim Oates:
Efficient Progressive Sampling. KDD 1999: 23-32 - [c17]Tom Fawcett, Foster J. Provost:
Activity Monitoring: Noticing Interesting Changes in Behavior. KDD 1999: 53-62 - 1998
- [j6]Tom Fawcett, Ira J. Haimowitz, Foster J. Provost, Salvatore J. Stolfo:
AI Approaches to Fraud Detection and Risk Management. AI Mag. 19(2): 107-108 (1998) - [j5]Foster J. Provost, Ron Kohavi:
Guest Editors' Introduction: On Applied Research in Machine Learning. Mach. Learn. 30(2-3): 127-132 (1998) - [c16]Foster J. Provost, Tom Fawcett:
Robust Classification Systems for Imprecise Environments. AAAI/IAAI 1998: 706-713 - [c15]Foster J. Provost, Tom Fawcett, Ron Kohavi:
The Case against Accuracy Estimation for Comparing Induction Algorithms. ICML 1998: 445-453 - 1997
- [j4]Tom Fawcett, Foster J. Provost:
Adaptive Fraud Detection. Data Min. Knowl. Discov. 1(3): 291-316 (1997) - [c14]Foster J. Provost, Tom Fawcett:
Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. KDD 1997: 43-48 - [c13]John M. Aronis, Foster J. Provost:
Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation. KDD 1997: 119-122 - [c12]Foster J. Provost, Venkateswarlu Kolluri:
Scaling Up Inductive Algorithms: An Overview. KDD 1997: 239-242 - 1996
- [j3]Foster J. Provost, John M. Aronis:
Scaling Up Inductive Learning with Massive Parallelism. Mach. Learn. 23(1): 33-46 (1996) - [c11]Foster J. Provost, Daniel N. Hennessy:
Scaling Up: Distributed Machine Learning with Cooperation. AAAI/IAAI, Vol. 1 1996: 74-79 - [c10]Tom Fawcett, Foster J. Provost:
Combining Data Mining and Machine Learning for Effective User Profiling. KDD 1996: 8-13 - [c9]John M. Aronis, Foster J. Provost, Bruce G. Buchanan:
Exploiting Background Knowledge in Automated Discovery. KDD 1996: 355-358 - 1995
- [j2]Foster J. Provost, Bruce G. Buchanan:
Inductive Policy: The Pragmatics of Bias Selection. Mach. Learn. 20(1-2): 35-61 (1995) - 1994
- [c8]Foster J. Provost, Daniel N. Hennessy:
Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism. ISMB 1994: 340-347 - [c7]John M. Aronis, Foster J. Provost:
Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning. KDD Workshop 1994: 347-358 - 1993
- [c6]Foster J. Provost:
Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias. AAAI 1993: 749-755 - [c5]Andrea Pohoreckyj Danyluk, Foster J. Provost:
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network. ICML 1993: 81-88 - 1992
- [j1]