Foster J. Provost

According to our database, Foster J. Provost authored at least 114 papers between 1990 and 2024.

Collaborative distances:

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2024

Causal Fine-Tuning and Effect Calibration of Non-Causal Predictive Models.

Carlos Fernández-Loría, Yanfang Hou, Foster J. Provost, Jennifer Hill

CoRR, 2024

Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete?

Connor Douglas, Foster J. Provost, Arun Sundarajan

Proceedings of the 45th International Conference on Information Systems, 2024

2023

A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation.

Carlos Fernández-Loría, Foster J. Provost, Jesse Anderton, Benjamin A. Carterette, Praveen Chandar

Inf. Syst. Res., 2023

Who's Watching TV?

Jessica Clark, Jean-François Paiement, Foster J. Provost

Inf. Syst. Res., 2023

The Impact of Cloaking Digital Footprints on User Privacy and Personalization.

Sofie Goethals, Sandra C. Matz, Foster J. Provost, Yanou Ramon, David Martens

CoRR, 2023

2022

Causal Classification: Treatment Effect Estimation vs. Outcome Prediction.

Carlos Fernández-Loría, Foster J. Provost

J. Mach. Learn. Res., 2022

2021

Node classification over bipartite graphs through projection.

Marija Stankova, Stiene Praet, David Martens, Foster J. Provost

Mach. Learn., 2021

Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters.

Carlos Fernández-Loría, Foster J. Provost

CoRR, 2021

2020

In memory of Tom Fawcett.

Foster J. Provost

Mach. Learn., 2020

A benchmarking study of classification techniques for behavioral data.

Sofie De Cnudde, David Martens, Theodoros Evgeniou, Foster J. Provost

Int. J. Data Sci. Anal., 2020

Methods for Individual Treatment Assignment: An Application and Comparison for Playlist Generation.

Carlos Fernandez, Foster J. Provost, Jesse Anderton, Benjamin A. Carterette, Praveen Chandar

CoRR, 2020

Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach.

Carlos Fernandez, Foster J. Provost, Xintian Han

CoRR, 2020

A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C.

Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou

Adv. Data Anal. Classif., 2020

Data Science for the Real Estate Industry.

Ron Bekkerman, Vanja Josifovski, Foster J. Provost

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Combining Observational and Experimental Data to Improve Large-Scale Decision-Making.

Carlos Fernández-Loría, Foster J. Provost

Proceedings of the 41st International Conference on Information Systems, 2020

2019

Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data.

Jessica Clark, Foster J. Provost

Data Min. Knowl. Discov., 2019

Counterfactual Explanation Algorithms for Behavioral and Textual Data.

Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou

CoRR, 2019

Deep Learning on Big, Sparse, Behavioral Data.

Sofie De Cnudde, Yanou Ramon, David Martens, Foster J. Provost

Big Data, 2019

Counterfactual Explanations for Data-Driven Decisions.

Carlos Fernandez, Foster J. Provost, Xintian Han

Proceedings of the 40th International Conference on Information Systems, 2019

2018

Wallenius Bayes.

Enric Junqué de Fortuny, David Martens, Foster J. Provost

Mach. Learn., 2018

Data-Driven Investment Strategies for Peer-to-Peer Lending: A Case Study for Teaching Data Science.

Maxime C. Cohen, C. Daniel Guetta, Kevin Jiao, Foster J. Provost

Big Data, 2018

Societal Impact of Data Science and Artificial Intelligence.

Foster J. Provost, James Hodson, Jeannette M. Wing, Qiang Yang, Jennifer Neville

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017

Cost-Effective Quality Assurance in Crowd Labeling.

Jing Wang, Panagiotis G. Ipeirotis, Foster J. Provost

Inf. Syst. Res., 2017

Big Data, Data Science, and Civil Rights.

Solon Barocas, Elizabeth Bradley, Vasant G. Honavar, Foster J. Provost

CoRR, 2017

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

Daizhuo Chen, Samuel P. Fraiberger, Robert Moakler, Foster J. Provost

Big Data, 2017

2016

Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics.

David Martens, Foster J. Provost, Jessica Clark, Enric Junqué de Fortuny

MIS Q., 2016

Explaining Classification Models Built on High-Dimensional Sparse Data.

Julie Moeyersoms, Brian Dalessandro, Foster J. Provost, David Martens

CoRR, 2016

The Predictive Power of Massive Data about our Fine-Grained Behavior.

Foster J. Provost

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 2016

2015

Beat the Machine: Challenging Humans to Find a Predictive Model's "Unknown Unknowns".

Joshua Attenberg, Panos Ipeirotis, Foster J. Provost

ACM J. Data Inf. Qual., 2015

Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design.

Foster J. Provost, David Martens, Alan Murray

Inf. Syst. Res., 2015

Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies.

Brian Dalessandro, Rod Hook, Claudia Perlich, Foster J. Provost

Big Data, 2015

Measuring Causal Impact of Online Actions via Natural Experiments: Application to Display Advertising.

Daniel N. Hill, Robert Moakler, Alan E. Hubbard, Vadim Tsemekhman, Foster J. Provost, Kiril Tsemekhman

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Iteratively refining SVMs using priors.

Enric Junqué de Fortuny, Theodoros Evgeniou, David Martens, Foster J. Provost

Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015

2014

Machine learning for targeted display advertising: transfer learning in action.

Claudia Perlich, Brian Dalessandro, Troy Raeder, Ori Stitelman, Foster J. Provost

Mach. Learn., 2014

Explaining Data-Driven Document Classifications.

David Martens, Foster J. Provost

MIS Q., 2014

Repeated labeling using multiple noisy labelers.

Panagiotis G. Ipeirotis, Foster J. Provost, Victor S. Sheng, Jing Wang

Data Min. Knowl. Discov., 2014

A Data Scientist's Guide to Start-Ups.

Foster J. Provost, Geoffrey I. Webb, Ron Bekkerman, Oren Etzioni, Usama M. Fayyad, Claudia Perlich

Big Data, 2014

Authors' Response to Gong's, "Comment on Data Science and its Relationship to Big Data and Data-Driven Decision Making".

Foster J. Provost, Tom Fawcett

Big Data, 2014

ACM SIGKDD 2014 to be Held August 24-27 in Manhattan.

Foster J. Provost

Big Data, 2014

Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground truth.

Melinda Han Williams, Claudia Perlich, Brian Dalessandro, Foster J. Provost

Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, 2014

Corporate residence fraud detection.

Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster J. Provost, David Martens

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Scalable hands-free transfer learning for online advertising.

Brian Dalessandro, Daizhuo Chen, Troy Raeder, Claudia Perlich, Melinda Han Williams, Foster J. Provost

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

2013

Research Commentary - Information in Digital, Economic, and Social Networks.

Arun Sundararajan, Foster J. Provost, Gal Oestreicher-Singer, Sinan Aral

Inf. Syst. Res., 2013

Data Science and its Relationship to Big Data and Data-Driven Decision Making.

Foster J. Provost, Tom Fawcett

Big Data, 2013

Predictive Modeling With Big Data: <i>Is Bigger Really Better</i>?

Enric Junqué de Fortuny, David Martens, Foster J. Provost

Big Data, 2013

Using co-visitation networks for detecting large scale online display advertising exchange fraud.

Ori Stitelman, Claudia Perlich, Brian Dalessandro, Rod Hook, Troy Raeder, Foster J. Provost

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Scalable supervised dimensionality reduction using clustering.

Troy Raeder, Claudia Perlich, Brian Dalessandro, Ori Stitelman, Foster J. Provost

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Panel: a data scientist's guide to making money from start-ups.

Foster J. Provost, Geoffrey I. Webb

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Hyperlocal: inferring location of IP addresses in real-time bid requests for mobile ads.

Long T. Le, Tina Eliassi-Rad, Foster J. Provost, Lauren Moores

Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, 2013

2012

Design principles of massive, robust prediction systems.

Troy Raeder, Ori Stitelman, Brian Dalessandro, Claudia Perlich, Foster J. Provost

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Bid optimizing and inventory scoring in targeted online advertising.

Claudia Perlich, Brian Dalessandro, Rod Hook, Ori Stitelman, Troy Raeder, Foster J. Provost

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Causally motivated attribution for online advertising.

Brian Dalessandro, Claudia Perlich, Ori Stitelman, Foster J. Provost

Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy, 2012

2011

Online active inference and learning.

Josh Attenberg, Foster J. Provost

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

Beat the Machine: Challenging Workers to Find the Unknown Unknowns.

Josh Attenberg, Panagiotis G. Ipeirotis, Foster J. Provost

Proceedings of the Human Computation, 2011

2010

Inactive learning?: difficulties employing active learning in practice.

Josh Attenberg, Foster J. Provost

SIGKDD Explor., 2010

A Unified Approach to Active Dual Supervision for Labeling Features and Examples.

Josh Attenberg, Prem Melville, Foster J. Provost

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance.

Josh Attenberg, Foster J. Provost

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

Quality management on Amazon Mechanical Turk.

Panagiotis G. Ipeirotis, Foster J. Provost, Jing Wang

Proceedings of the ACM SIGKDD Workshop on Human Computation, 2010

2009

Active Feature-Value Acquisition.

Maytal Saar-Tsechansky, Prem Melville, Foster J. Provost

Manag. Sci., 2009

Audience selection for on-line brand advertising: privacy-friendly social network targeting.

Foster J. Provost, Brian Dalessandro, Rod Hook, Xiaohan Zhang, Alan Murray

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

Brand advertising, on-line audiences, and social media: invited talk.

Foster J. Provost

Proceedings of the 3rd ACM SIGKDD Workshop on Data Mining and Audience Intelligence for Advertising, 2009

2008

Get another label? improving data quality and data mining using multiple, noisy labelers.

Victor S. Sheng, Foster J. Provost, Panagiotis G. Ipeirotis

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

2007

Handling Missing Values when Applying Classification Models.

Maytal Saar-Tsechansky, Foster J. Provost

J. Mach. Learn. Res., 2007

Classification in Networked Data: A Toolkit and a Univariate Case Study.

Sofus A. Macskassy, Foster J. Provost

J. Mach. Learn. Res., 2007

Decision-Centric Active Learning of Binary-Outcome Models.

Maytal Saar-Tsechansky, Foster J. Provost

Inf. Syst. Res., 2007

Modeling complex networks for electronic commerce.

Foster J. Provost, Arun Sundararajan

Proceedings of the Proceedings 8th ACM Conference on Electronic Commerce (EC-2007), 2007

Learning and Inference in Massive Social Networks.

Shawndra Hill, Foster J. Provost, Chris Volinsky

Proceedings of the Mining and Learning with Graphs, 2007

Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce.

Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky

Proceedings of the 9th International Conference on Electronic Commerce: The Wireless World of Electronic Commerce, 2007

2006

Distribution-based aggregation for relational learning with identifier attributes.

Claudia Perlich, Foster J. Provost

Mach. Learn., 2006

A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring.

Sofus Attila Macskassy, Foster J. Provost

Proceedings of the Statistical Network Analysis: Models, Issues, and New Directions, 2006

2005

Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification.

Abraham Bernstein, Foster J. Provost, Shawndra Hill

IEEE Trans. Knowl. Data Eng., 2005

ROC confidence bands: an empirical evaluation.

Sofus A. Macskassy, Foster J. Provost, Saharon Rosset

Proceedings of the Machine Learning, 2005

An Expected Utility Approach to Active Feature-Value Acquisition.

Prem Melville, Foster J. Provost, Raymond J. Mooney

Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 2005

2004

Active Sampling for Class Probability Estimation and Ranking.

Maytal Saar-Tsechansky, Foster J. Provost

Mach. Learn., 2004

Confidence Bands for ROC Curves: Methods and an Empirical Study.

Sofus A. Macskassy, Foster J. Provost

Proceedings of the ROC Analysis in Artificial Intelligence, 1st International Workshop, 2004

Active Feature-Value Acquisition for Classifier Induction.

Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney

Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 2004

Knowledge Discovery Using Concept-Class Taxonomies.

Venkateswarlu Kolluri, Foster J. Provost, Bruce G. Buchanan, Douglas Metzler

Proceedings of the AI 2004: Advances in Artificial Intelligence, 2004

2003

Predicting citation rates for physics papers: constructing features for an ordered probit model.

Claudia Perlich, Foster J. Provost, Sofus A. Macskassy

SIGKDD Explor., 2003

The myth of the double-blind review?: author identification using only citations.

Shawndra Hill, Foster J. Provost

SIGKDD Explor., 2003

Tree Induction for Probability-Based Ranking.

Foster J. Provost, Pedro M. Domingos

Mach. Learn., 2003

Tree Induction vs. Logistic Regression: A Learning-Curve Analysis.

Claudia Perlich, Foster J. Provost, Jeffrey S. Simonoff

J. Mach. Learn. Res., 2003

Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction.

Gary M. Weiss, Foster J. Provost

J. Artif. Intell. Res., 2003

Aggregation-based feature invention and relational concept classes.

Claudia Perlich, Foster J. Provost

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24, 2003

2001

Robust Classification for Imprecise Environments.

Foster J. Provost, Tom Fawcett

Mach. Learn., 2001

Applications of Data Mining to Electronic Commerce.

Ron Kohavi, Foster J. Provost

Data Min. Knowl. Discov., 2001

Intelligent Information Triage.

Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar

Proceedings of the SIGIR 2001: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2001

Active Learning for Class Probability Estimation and Ranking.

Maytal Saar-Tsechansky, Foster J. Provost

Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 2001

2000

Discovering Interesting Patterns for Investment Decision Making with GLOWER - A Genetic Learner Overlaid with Entropy Reduction.

Vasant Dhar, Dashin Chou, Foster J. Provost

Data Min. Knowl. Discov., 2000

1999

Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study.

Foster J. Provost, Andrea Pohoreckyj Danyluk

Informatica (Slovenia), 1999

A Survey of Methods for Scaling Up Inductive Algorithms.

Foster J. Provost, Venkateswarlu Kolluri

Data Min. Knowl. Discov., 1999

Efficient Progressive Sampling.

Foster J. Provost, David D. Jensen, Tim Oates

Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999

Activity Monitoring: Noticing Interesting Changes in Behavior.

Tom Fawcett, Foster J. Provost

Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999

1998

Guest Editors' Introduction: On Applied Research in Machine Learning.

Foster J. Provost, Ron Kohavi

Mach. Learn., 1998

AI Approaches to Fraud Detection and Risk Management.

Tom Fawcett, Ira J. Haimowitz, Foster J. Provost, Salvatore J. Stolfo

AI Mag., 1998

The Case against Accuracy Estimation for Comparing Induction Algorithms.

Foster J. Provost, Tom Fawcett, Ron Kohavi

Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), 1998

Robust Classification Systems for Imprecise Environments.

Foster J. Provost, Tom Fawcett

Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, 1998

1997

Adaptive Fraud Detection.

Tom Fawcett, Foster J. Provost

Data Min. Knowl. Discov., 1997

Scaling Up Inductive Algorithms: An Overview.

Foster J. Provost, Venkateswarlu Kolluri

Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), 1997

Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions.

Foster J. Provost, Tom Fawcett

Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), 1997

Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation.

John M. Aronis, Foster J. Provost

Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), 1997

1996

Scaling Up Inductive Learning with Massive Parallelism.

Foster J. Provost, John M. Aronis

Mach. Learn., 1996

Combining Data Mining and Machine Learning for Effective User Profiling.

Tom Fawcett, Foster J. Provost

Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 1996

Exploiting Background Knowledge in Automated Discovery.

John M. Aronis, Foster J. Provost, Bruce G. Buchanan

Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 1996

Scaling Up: Distributed Machine Learning with Cooperation.

Foster J. Provost, Daniel N. Hennessy

Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, 1996

1995

Inductive Policy: The Pragmatics of Bias Selection.

Foster J. Provost, Bruce G. Buchanan

Mach. Learn., 1995

1994

Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning.

John M. Aronis, Foster J. Provost

Proceedings of the Knowledge Discovery in Databases: Papers from the 1994 AAAI Workshop, 1994

Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism.

Foster J. Provost, Daniel N. Hennessy

Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, 1994

1993

Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network.

Andrea Pohoreckyj Danyluk, Foster J. Provost

Proceedings of the Machine Learning, 1993

Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias.

Foster J. Provost

Proceedings of the 11th National Conference on Artificial Intelligence. Washington, 1993

1992

A Distributed Algorithm for Embedding Trees in Hypercubes with Modifications for Run-Time Fault Tolerance.

Foster J. Provost, Rami G. Melhem

J. Parallel Distributed Comput., 1992

ClimBS: Searching the Bias Space.

Foster J. Provost

Proceedings of the Fourth International Conference on Tools with Artificial Intelligence, 1992

Inductive Strengthening: the Effects of a Simple Heuristic for Restricting Hypothesis Space Search.

Foster J. Provost, Bruce G. Buchanan

Proceedings of the Analogical and Inductive Inference, 1992

Inductive Policy.

Foster J. Provost, Bruce G. Buchanan

Proceedings of the 10th National Conference on Artificial Intelligence, 1992

1990

RL4: a tool for knowledge-based induction.

Scott H. Clearwater, Foster J. Provost

Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence, 1990