Foster J. Provost

Publication Activity (10 Years)

Years Active: 1990-2023
Publications (10 Years): 30

Top Venues

CoRR

Big Data

KDD

Mach. Learn.

Publications

Sofie Goethals, Sandra C. Matz, Foster J. Provost, Yanou Ramon, David Martens
The Impact of Cloaking Digital Footprints on User Privacy and Personalization. CoRR (2023)
Carlos Fernández-Loría, Foster J. Provost
Causal Classification: Treatment Effect Estimation vs. Outcome Prediction. J. Mach. Learn. Res. 23 (2022)
Carlos Fernández-Loría, Foster J. Provost
Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters. CoRR (2021)
Marija Stankova, Stiene Praet, David Martens, Foster J. Provost
Node classification over bipartite graphs through projection. Mach. Learn. 110 (1) (2021)
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 (2020)
Ron Bekkerman, Vanja Josifovski, Foster J. Provost
Data Science for the Real Estate Industry. KDD (2020)
Foster J. Provost
In memory of Tom Fawcett. Mach. Learn. 109 (11) (2020)
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) (2020)
Carlos Fernández-Loría, Foster J. Provost
Combining Observational and Experimental Data to Improve Large-Scale Decision-Making. ICIS (2020)
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) (2020)
Carlos Fernandez, Foster J. Provost, Xintian Han
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach. CoRR (2020)
Sofie De Cnudde, Yanou Ramon, David Martens, Foster J. Provost
Deep Learning on Big, Sparse, Behavioral Data. Big Data 7 (4) (2019)
Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou
Counterfactual Explanation Algorithms for Behavioral and Textual Data. CoRR (2019)
Jessica Clark, Foster J. Provost
Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data. Data Min. Knowl. Discov. 33 (4) (2019)
Carlos Fernandez, Foster J. Provost, Xintian Han
Counterfactual Explanations for Data-Driven Decisions. ICIS (2019)
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) (2018)
Foster J. Provost, James Hodson, Jeannette M. Wing, Qiang Yang, Jennifer Neville
Societal Impact of Data Science and Artificial Intelligence. KDD (2018)
Enric Junqué de Fortuny, David Martens, Foster J. Provost
Wallenius Bayes. Mach. Learn. 107 (6) (2018)
Jing Wang, Panagiotis G. Ipeirotis, Foster J. Provost
Cost-Effective Quality Assurance in Crowd Labeling. Inf. Syst. Res. 28 (1) (2017)
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) (2017)
Solon Barocas, Elizabeth Bradley, Vasant G. Honavar, Foster J. Provost
Big Data, Data Science, and Civil Rights. CoRR (2017)
Daizhuo Chen, Samuel P. Fraiberger, Robert Moakler, Foster J. Provost
Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals. CoRR (2016)
Julie Moeyersoms, Brian Dalessandro, Foster J. Provost, David Martens
Explaining Classification Models Built on High-Dimensional Sparse Data. CoRR (2016)
Foster J. Provost
The Predictive Power of Massive Data about our Fine-Grained Behavior. WSDM (2016)
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) (2016)
Enric Junqué de Fortuny, Theodoros Evgeniou, David Martens, Foster J. Provost
Iteratively refining SVMs using priors. IEEE BigData (2015)
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) (2015)
Foster J. Provost, David Martens, Alan Murray
Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design. Inf. Syst. Res. 26 (2) (2015)
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) (2015)
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)
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) (2014)
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) (2014)
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) (2014)
David Martens, Foster J. Provost
Explaining Data-Driven Document Classifications. MIS Q. 38 (1) (2014)
Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster J. Provost, David Martens
Corporate residence fraud detection. KDD (2014)
Brian Dalessandro, Daizhuo Chen, Troy Raeder, Claudia Perlich, Melinda Han Williams, Foster J. Provost
Scalable hands-free transfer learning for online advertising. KDD (2014)
Panagiotis G. Ipeirotis, Foster J. Provost, Victor S. Sheng, Jing Wang
Repeated labeling using multiple noisy labelers. Data Min. Knowl. Discov. 28 (2) (2014)
Melinda Han Williams, Claudia Perlich, Brian Dalessandro, Foster J. Provost
Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground truth. ADKDD@KDD (2014)
Foster J. Provost
ACM SIGKDD 2014 to be Held August 24-27 in Manhattan. Big Data 2 (2) (2014)
Foster J. Provost, Tom Fawcett
Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data 1 (1) (2013)
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)
Enric Junqué de Fortuny, David Martens, Foster J. Provost
? Big Data 1 (4) (2013)
Foster J. Provost, Geoffrey I. Webb
Panel: a data scientist's guide to making money from start-ups. KDD (2013)
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)
Troy Raeder, Claudia Perlich, Brian Dalessandro, Ori Stitelman, Foster J. Provost
Scalable supervised dimensionality reduction using clustering. KDD (2013)
Arun Sundararajan, Foster J. Provost, Gal Oestreicher-Singer, Sinan Aral
Research Commentary - Information in Digital, Economic, and Social Networks. Inf. Syst. Res. 24 (4) (2013)
Troy Raeder, Ori Stitelman, Brian Dalessandro, Claudia Perlich, Foster J. Provost
Design principles of massive, robust prediction systems. KDD (2012)
Claudia Perlich, Brian Dalessandro, Rod Hook, Ori Stitelman, Troy Raeder, Foster J. Provost
Bid optimizing and inventory scoring in targeted online advertising. KDD (2012)
Brian Dalessandro, Claudia Perlich, Ori Stitelman, Foster J. Provost
Causally motivated attribution for online advertising. AdKDD@KDD (2012)
Josh Attenberg, Foster J. Provost
Online active inference and learning. KDD (2011)
Foster J. Provost, Gary M. Weiss
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction CoRR (2011)
Josh Attenberg, Panagiotis G. Ipeirotis, Foster J. Provost
Beat the Machine: Challenging Workers to Find the Unknown Unknowns. Human Computation (2011)
Panagiotis G. Ipeirotis, Foster J. Provost, Jing Wang
Quality management on Amazon Mechanical Turk. HCOMP@KDD (2010)
Josh Attenberg, Foster J. Provost
Inactive learning?: difficulties employing active learning in practice. SIGKDD Explor. 12 (2) (2010)
Josh Attenberg, Prem Melville, Foster J. Provost
A Unified Approach to Active Dual Supervision for Labeling Features and Examples. ECML/PKDD (1) (2010)
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)
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)
Foster J. Provost
Brand advertising, on-line audiences, and social media: invited talk. KDD Workshop on Data Mining and Audience Intelligence for Advertising (2009)
Maytal Saar-Tsechansky, Prem Melville, Foster J. Provost
Active Feature-Value Acquisition. Manag. Sci. 55 (4) (2009)
Victor S. Sheng, Foster J. Provost, Panagiotis G. Ipeirotis
Get another label? improving data quality and data mining using multiple, noisy labelers. KDD (2008)
Shawndra Hill, Foster J. Provost, Chris Volinsky
Learning and Inference in Massive Social Networks. MLG (2007)
Sofus A. Macskassy, Foster J. Provost
Classification in Networked Data: A Toolkit and a Univariate Case Study. J. Mach. Learn. Res. 8 (2007)
Maytal Saar-Tsechansky, Foster J. Provost
Decision-Centric Active Learning of Binary-Outcome Models. Inf. Syst. Res. 18 (1) (2007)
Foster J. Provost, Arun Sundararajan
Modeling complex networks for electronic commerce. EC (2007)
Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. ICEC (2007)
Maytal Saar-Tsechansky, Foster J. Provost
Handling Missing Values when Applying Classification Models. J. Mach. Learn. Res. 8 (2007)
Claudia Perlich, Foster J. Provost
Distribution-based aggregation for relational learning with identifier attributes. Mach. Learn. 62 (1-2) (2006)
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)
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) (2005)
Sofus A. Macskassy, Foster J. Provost, Saharon Rosset
ROC confidence bands: an empirical evaluation. ICML (2005)
Prem Melville, Foster J. Provost, Raymond J. Mooney
An Expected Utility Approach to Active Feature-Value Acquisition. ICDM (2005)
Maytal Saar-Tsechansky, Foster J. Provost
Active Sampling for Class Probability Estimation and Ranking. Mach. Learn. 54 (2) (2004)
Sofus A. Macskassy, Foster J. Provost
Confidence Bands for ROC Curves: Methods and an Empirical Study. ROCAI (2004)
Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney
Active Feature-Value Acquisition for Classifier Induction. ICDM (2004)
Venkateswarlu Kolluri, Foster J. Provost, Bruce G. Buchanan, Douglas Metzler
Knowledge Discovery Using Concept-Class Taxonomies. Australian Conference on Artificial Intelligence (2004)
Claudia Perlich, Foster J. Provost, Jeffrey S. Simonoff
Tree Induction vs. Logistic Regression: A Learning-Curve Analysis. J. Mach. Learn. Res. 4 (2003)
Foster J. Provost, Pedro M. Domingos
Tree Induction for Probability-Based Ranking. Mach. Learn. 52 (3) (2003)
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) (2003)
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 (2003)
Shawndra Hill, Foster J. Provost
The myth of the double-blind review?: author identification using only citations. SIGKDD Explor. 5 (2) (2003)
Claudia Perlich, Foster J. Provost
Aggregation-based feature invention and relational concept classes. KDD (2003)
Ron Kohavi, Foster J. Provost
Applications of Data Mining to Electronic Commerce. Data Min. Knowl. Discov. 5 (1/2) (2001)
Maytal Saar-Tsechansky, Foster J. Provost
Active Learning for Class Probability Estimation and Ranking. IJCAI (2001)
Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar
Intelligent Information Triage. SIGIR (2001)
Foster J. Provost, Tom Fawcett
Robust Classification for Imprecise Environments. Mach. Learn. 42 (3) (2001)
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) (2000)
Ron Kohavi, Foster J. Provost
Applications of Data Mining to Electronic Commerce CoRR (2000)
Foster J. Provost, Tom Fawcett
Robust Classification for Imprecise Environments CoRR (2000)
Foster J. Provost, David D. Jensen, Tim Oates
Efficient Progressive Sampling. KDD (1999)
Tom Fawcett, Foster J. Provost
Activity Monitoring: Noticing Interesting Changes in Behavior. KDD (1999)
Foster J. Provost, Venkateswarlu Kolluri
A Survey of Methods for Scaling Up Inductive Algorithms. Data Min. Knowl. Discov. 3 (2) (1999)
Foster J. Provost, Andrea Pohoreckyj Danyluk
Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study. Informatica (Slovenia) 23 (1) (1999)
Tom Fawcett, Ira J. Haimowitz, Foster J. Provost, Salvatore J. Stolfo
AI Approaches to Fraud Detection and Risk Management. AI Mag. 19 (2) (1998)
Foster J. Provost, Tom Fawcett
Robust Classification Systems for Imprecise Environments. AAAI/IAAI (1998)
Foster J. Provost, Tom Fawcett, Ron Kohavi
The Case against Accuracy Estimation for Comparing Induction Algorithms. ICML (1998)
Foster J. Provost, Ron Kohavi
Guest Editors' Introduction: On Applied Research in Machine Learning. Mach. Learn. 30 (2-3) (1998)
Tom Fawcett, Foster J. Provost
Adaptive Fraud Detection. Data Min. Knowl. Discov. 1 (3) (1997)
Foster J. Provost, Tom Fawcett
Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. KDD (1997)
John M. Aronis, Foster J. Provost
Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation. KDD (1997)
Foster J. Provost, Venkateswarlu Kolluri
Scaling Up Inductive Algorithms: An Overview. KDD (1997)

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Publication Activity (10 Years)

Top Topics

Top Venues

Foster J. Provost