RL4: a tool for knowledge-based induction. - Scott H. Clearwater; Foster J. Provost (TAI, 1990)

Inductive Policy. - Foster J. Provost; Bruce G. Buchanan (AAAI, 1992)

Inductive Strengthening: the Effects of a Simple Heuristic for Restricting Hypothesis Space Search. - Foster J. Provost; Bruce G. Buchanan (AII, 1992)

ClimBS: Searching the Bias Space. - Foster J. Provost (ICTAI, 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)

Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias. - Foster J. Provost (AAAI, 1993)

Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network. - Andrea Pohoreckyj Danyluk; Foster J. Provost (ICML, 1993)

Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism. - Foster J. Provost; Daniel N. Hennessy (ISMB, 1994)

Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning. - John M. Aronis; Foster J. Provost (KDD Workshop, 1994)

Inductive Policy: The Pragmatics of Bias Selection. - Foster J. Provost; Bruce G. Buchanan (Mach. Learn., 1995)

Scaling Up: Distributed Machine Learning with Cooperation. - Foster J. Provost; Daniel N. Hennessy (AAAI/IAAI, Vol. 1, 1996)

Exploiting Background Knowledge in Automated Discovery. - John M. Aronis; Foster J. Provost; Bruce G. Buchanan (KDD, 1996)

Combining Data Mining and Machine Learning for Effective User Profiling. - Tom Fawcett; Foster J. Provost (KDD, 1996)

Scaling Up Inductive Learning with Massive Parallelism. - Foster J. Provost; John M. Aronis (Mach. Learn., 1996)

Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation. - John M. Aronis; Foster J. Provost (KDD, 1997)

Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. - Foster J. Provost; Tom Fawcett (KDD, 1997)

Scaling Up Inductive Algorithms: An Overview. - Foster J. Provost; Venkateswarlu Kolluri (KDD, 1997)

Adaptive Fraud Detection. - Tom Fawcett; Foster J. Provost (Data Min. Knowl. Discov., 1997)

Robust Classification Systems for Imprecise Environments. - Foster J. Provost; Tom Fawcett (AAAI/IAAI, 1998)

The Case against Accuracy Estimation for Comparing Induction Algorithms. - Foster J. Provost; Tom Fawcett; Ron Kohavi (ICML, 1998)

AI Approaches to Fraud Detection and Risk Management. - Tom Fawcett; Ira J. Haimowitz; Foster J. Provost; Salvatore J. Stolfo (AI Mag., 1998)

Guest Editors' Introduction: On Applied Research in Machine Learning. - Foster J. Provost; Ron Kohavi (Mach. Learn., 1998)

Activity Monitoring: Noticing Interesting Changes in Behavior. - Tom Fawcett; Foster J. Provost (KDD, 1999)

Efficient Progressive Sampling. - Foster J. Provost; David D. Jensen; Tim Oates 001 (KDD, 1999)

A Survey of Methods for Scaling Up Inductive Algorithms. - Foster J. Provost; Venkateswarlu Kolluri (Data Min. Knowl. Discov., 1999)

Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study. - Foster J. Provost; Andrea Pohoreckyj Danyluk (Informatica (Slovenia), 1999)

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)

Active Learning for Class Probability Estimation and Ranking. - Maytal Saar-Tsechansky; Foster J. Provost (IJCAI, 2001)

Intelligent Information Triage. - Sofus A. Macskassy; Haym Hirsh; Foster J. Provost; Ramesh Sankaranarayanan; Vasant Dhar (SIGIR, 2001)

Applications of Data Mining to Electronic Commerce. - Ron Kohavi; Foster J. Provost (Data Min. Knowl. Discov., 2001)

Robust Classification for Imprecise Environments. - Foster J. Provost; Tom Fawcett (Mach. Learn., 2001)

Aggregation-based feature invention and relational concept classes. - Claudia Perlich; Foster J. Provost (KDD, 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)

Tree Induction vs. Logistic Regression: A Learning-Curve Analysis. - Claudia Perlich; Foster J. Provost; Jeffrey S. Simonoff (J. Mach. Learn. Res., 2003)

Tree Induction for Probability-Based Ranking. - Foster J. Provost; Pedro M. Domingos (Mach. Learn., 2003)

The myth of the double-blind review?: author identification using only citations. - Shawndra Hill; Foster J. Provost (SIGKDD Explor., 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)

Knowledge Discovery Using Concept-Class Taxonomies. - Venkateswarlu Kolluri; Foster J. Provost; Bruce G. Buchanan; Douglas Metzler (Australian Conference on Artificial Intelligence, 2004)

Active Feature-Value Acquisition for Classifier Induction. - Prem Melville; Maytal Saar-Tsechansky; Foster J. Provost; Raymond J. Mooney (ICDM, 2004)

Confidence Bands for ROC Curves: Methods and an Empirical Study. - Sofus A. Macskassy; Foster J. Provost (ROCAI, 2004)

Active Sampling for Class Probability Estimation and Ranking. - Maytal Saar-Tsechansky; Foster J. Provost (Mach. Learn., 2004)

An Expected Utility Approach to Active Feature-Value Acquisition. - Prem Melville; Foster J. Provost; Raymond J. Mooney (ICDM, 2005)

ROC confidence bands: an empirical evaluation. - Sofus A. Macskassy; Foster J. Provost; Saharon Rosset (ICML, 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)

A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring. - Sofus Attila Macskassy; Foster J. Provost (SNA@ICML, 2006)

Distribution-based aggregation for relational learning with identifier attributes. - Claudia Perlich; Foster J. Provost (Mach. Learn., 2006)

Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. - Foster J. Provost; Prem Melville; Maytal Saar-Tsechansky (ICEC, 2007)

Learning and Inference in Massive Social Networks. - Shawndra Hill; Foster J. Provost; Chris Volinsky (MLG, 2007)

Modeling complex networks for electronic commerce. - Foster J. Provost; Arun Sundararajan (EC, 2007)

Decision-Centric Active Learning of Binary-Outcome Models. - Maytal Saar-Tsechansky; Foster J. Provost (Inf. Syst. Res., 2007)

Classification in Networked Data: A Toolkit and a Univariate Case Study. - Sofus A. Macskassy; Foster J. Provost (J. Mach. Learn. Res., 2007)

Handling Missing Values when Applying Classification Models. - Maytal Saar-Tsechansky; Foster J. Provost (J. Mach. Learn. Res., 2007)

Get another label? improving data quality and data mining using multiple, noisy labelers. - Victor S. Sheng; Foster J. Provost; Panagiotis G. Ipeirotis (KDD, 2008)

Brand advertising, on-line audiences, and social media: invited talk. - Foster J. Provost (KDD Workshop on Data Mining and Audience Intelligence for Advertising, 2009)

Audience selection for on-line brand advertising: privacy-friendly social network targeting. - Foster J. Provost; Brian Dalessandro; Rod Hook; Xiaohan Zhang; Alan Murray (KDD, 2009)

Active Feature-Value Acquisition. - Maytal Saar-Tsechansky; Prem Melville; Foster J. Provost (Manag. Sci., 2009)

Quality management on Amazon Mechanical Turk. - Panagiotis G. Ipeirotis; Foster J. Provost; Jing Wang 019 (HCOMP@KDD, 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 (KDD, 2010)

A Unified Approach to Active Dual Supervision for Labeling Features and Examples. - Josh Attenberg; Prem Melville; Foster J. Provost (ECML/PKDD (1), 2010)

Inactive learning?: difficulties employing active learning in practice. - Josh Attenberg; Foster J. Provost (SIGKDD Explor., 2010)

Beat the Machine: Challenging Workers to Find the Unknown Unknowns. - Josh Attenberg; Panagiotis G. Ipeirotis; Foster J. Provost (Human Computation, 2011)

Online active inference and learning. - Josh Attenberg; Foster J. Provost (KDD, 2011)

Causally motivated attribution for online advertising. - Brian Dalessandro; Claudia Perlich; Ori Stitelman; Foster J. Provost (AdKDD@KDD, 2012)

Bid optimizing and inventory scoring in targeted online advertising. - Claudia Perlich; Brian Dalessandro; Rod Hook; Ori Stitelman; Troy Raeder; Foster J. Provost (KDD, 2012)

Design principles of massive, robust prediction systems. - Troy Raeder; Ori Stitelman; Brian Dalessandro; Claudia Perlich; Foster J. Provost (KDD, 2012)

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 (LBSN, 2013)

Panel: a data scientist's guide to making money from start-ups. - Foster J. Provost; Geoffrey I. Webb (KDD, 2013)

Scalable supervised dimensionality reduction using clustering. - Troy Raeder; Claudia Perlich; Brian Dalessandro; Ori Stitelman; Foster J. Provost (KDD, 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 (KDD, 2013)

Predictive Modeling With Big Data: Is Bigger Really Better? - Enric Junqué de Fortuny; David Martens; Foster J. Provost (Big Data, 2013)

Data Science and its Relationship to Big Data and Data-Driven Decision Making. - Foster J. Provost; Tom Fawcett (Big Data, 2013)

Research Commentary - Information in Digital, Economic, and Social Networks. - Arun Sundararajan; Foster J. Provost; Gal Oestreicher-Singer; Sinan Aral (Inf. Syst. Res., 2013)

Scalable hands-free transfer learning for online advertising. - Brian Dalessandro; Daizhuo Chen; Troy Raeder; Claudia Perlich; Melinda Han Williams; Foster J. Provost (KDD, 2014)

Corporate residence fraud detection. - Enric Junqué de Fortuny; Marija Stankova; Julie Moeyersoms; Bart Minnaert; Foster J. Provost; David Martens (KDD, 2014)

Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground truth. - Melinda Han Williams; Claudia Perlich; Brian Dalessandro; Foster J. Provost (ADKDD@KDD, 2014)

ACM SIGKDD 2014 to be Held August 24-27 in Manhattan. - Foster J. Provost (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)

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)

Repeated labeling using multiple noisy labelers. - Panagiotis G. Ipeirotis; Foster J. Provost; Victor S. Sheng; Jing Wang 019 (Data Min. Knowl. Discov., 2014)

Explaining Data-Driven Document Classifications. - David Martens; Foster J. Provost (MIS Q., 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)

Iteratively refining SVMs using priors. - Enric Junqué de Fortuny; Theodoros Evgeniou; David Martens; Foster J. Provost (IEEE BigData, 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 (KDD, 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)

Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design. - Foster J. Provost; David Martens; Alan Murray (Inf. Syst. Res., 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)

The Predictive Power of Massive Data about our Fine-Grained Behavior. - Foster J. Provost (WSDM, 2016)

Explaining Classification Models Built on High-Dimensional Sparse Data. - Julie Moeyersoms; Brian Dalessandro; Foster J. Provost; David Martens (CoRR, 2016)

Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics. - David Martens; Foster J. Provost; Jessica Clark 002; Enric Junqué de Fortuny (MIS Q., 2016)

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals. - Daizhuo Chen; Samuel P. Fraiberger; Robert Moakler; Foster J. Provost (Big Data, 2017)

Big Data, Data Science, and Civil Rights. - Solon Barocas; Elizabeth Bradley; Vasant G. Honavar; Foster J. Provost (CoRR, 2017)

Cost-Effective Quality Assurance in Crowd Labeling. - Jing Wang 019; Panagiotis G. Ipeirotis; Foster J. Provost (Inf. Syst. Res., 2017)

Societal Impact of Data Science and Artificial Intelligence. - Foster J. Provost; James Hodson; Jeannette M. Wing; Qiang Yang 001; Jennifer Neville (KDD, 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)

Wallenius Bayes. - Enric Junqué de Fortuny; David Martens; Foster J. Provost (Mach. Learn., 2018)

Counterfactual Explanations for Data-Driven Decisions. - Carlos Fernandez; Foster J. Provost; Xintian Han (ICIS, 2019)

Deep Learning on Big, Sparse, Behavioral Data. - Sofie De Cnudde; Yanou Ramon; David Martens; Foster J. Provost (Big Data, 2019)

Counterfactual Explanation Algorithms for Behavioral and Textual Data. - Yanou Ramon; David Martens; Foster J. Provost; Theodoros Evgeniou (CoRR, 2019)

Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data. - Jessica Clark 002; Foster J. Provost (Data Min. Knowl. Discov., 2019)

Combining Observational and Experimental Data to Improve Large-Scale Decision-Making. - Carlos Fernández-Loría; Foster J. Provost (ICIS, 2020)

Data Science for the Real Estate Industry. - Ron Bekkerman; Vanja Josifovski; Foster J. Provost (KDD, 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)

Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach. - Carlos Fernandez; Foster J. Provost; Xintian Han (CoRR, 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)

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)

In memory of Tom Fawcett. - Foster J. Provost (Mach. Learn., 2020)

Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters. - Carlos Fernández-Loría; Foster J. Provost (CoRR, 2021)

Node classification over bipartite graphs through projection. - Marija Stankova; Stiene Praet; David Martens; Foster J. Provost (Mach. Learn., 2021)

Causal Classification: Treatment Effect Estimation vs. Outcome Prediction. - Carlos Fernández-Loría; Foster J. Provost (J. Mach. Learn. Res., 2022)

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)

Who's Watching TV? - Jessica Clark 002; Jean-François Paiement; Foster J. Provost (Inf. Syst. Res., 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)

Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete? - Connor Douglas; Foster J. Provost; Arun Sundarajan (ICIS, 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 Sundararajan (CoRR, 2024)

Leveraging AI for Management Decision-Making (Dagstuhl Seminar 24342). - Stefan Feuerriegel; Foster J. Provost; Galit Shmueli (Dagstuhl Reports, 2024)

What If the Prompt Were Different? Counterfactual Explanations for the Characteristics of Generative Outputs. - Sofie Goethals; João Sedoc; Foster J. Provost (UMAP (Adjunct Publication), 2025)

Beware of "Explanations" of AI. - David Martens; Galit Shmueli; Theodoros Evgeniou; Kevin Bauer; Christian Janiesch; Stefan Feuerriegel; Sebastian Gabel; Sofie Goethals; Travis Greene; Nadja Klein; Mathias Kraus; Niklas Kühl 001; et al. (CoRR, 2025)