Foster J. Provost

127 papers A* 33A 2B 2C 4Journal 66Unranked 18

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Prompt-Counterfactual Explanations for Generative AI System Behavior. CoRR Sofie Goethals, Foster J. Provost, João Sedoc
2025	J	`jnl`	Beware of "Explanations" of AI. CoRR David Martens, Galit Shmueli, Theodoros Evgeniou, Kevin Bauer, Christian Janiesch, Stefan Feuerriegel, Sebastian Gabel, Sofie Goethals, Travis Greene, Nadja Klein, Mathias Kraus, Niklas Kühl, Claudia Perlich, Wouter Verbeke, Alona Zharova, Patrick Zschech, Foster J. Provost
2025	—	`conf`	What If the Prompt Were Different? Counterfactual Explanations for the Characteristics of Generative Outputs. UMAP (Adjunct Publication) Sofie Goethals, João Sedoc, Foster J. Provost
2024	J	`jnl`	Causal Fine-Tuning and Effect Calibration of Non-Causal Predictive Models. CoRR Carlos Fernández-Loría, Yanfang Hou, Foster J. Provost, Jennifer Hill
2024	J	`jnl`	Leveraging AI for Management Decision-Making (Dagstuhl Seminar 24342). Dagstuhl Reports Stefan Feuerriegel, Foster J. Provost, Galit Shmueli
2024	C	`conf`	Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete? ICIS Connor Douglas, Foster J. Provost, Arun Sundarajan
2024	J	`jnl`	Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete? CoRR Connor Douglas, Foster J. Provost, Arun Sundararajan
2023	J	`jnl`	A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation. Inf. Syst. Res. Carlos Fernández-Loría, Foster J. Provost, Jesse Anderton, Benjamin A. Carterette, Praveen Chandar
2023	J	`jnl`	The Impact of Cloaking Digital Footprints on User Privacy and Personalization. CoRR Sofie Goethals, Sandra C. Matz, Foster J. Provost, Yanou Ramon, David Martens
2023	J	`jnl`	Who's Watching TV? Inf. Syst. Res. Jessica Clark, Jean-François Paiement, Foster J. Provost
2022	J	`jnl`	Causal Classification: Treatment Effect Estimation vs. Outcome Prediction. J. Mach. Learn. Res. Carlos Fernández-Loría, Foster J. Provost
2021	J	`jnl`	Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters. CoRR Carlos Fernández-Loría, Foster J. Provost
2021	J	`jnl`	Node classification over bipartite graphs through projection. Mach. Learn. Marija Stankova, Stiene Praet, David Martens, Foster J. Provost
2020	J	`jnl`	A benchmarking study of classification techniques for behavioral data. Int. J. Data Sci. Anal. Sofie De Cnudde, David Martens, Theodoros Evgeniou, Foster J. Provost
2020	J	`jnl`	A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C. Adv. Data Anal. Classif. Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou
2020	C	`conf`	Combining Observational and Experimental Data to Improve Large-Scale Decision-Making. ICIS Carlos Fernández-Loría, Foster J. Provost
2020	A*	`conf`	Data Science for the Real Estate Industry. KDD Ron Bekkerman, Vanja Josifovski, Foster J. Provost
2020	J	`jnl`	Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach. CoRR Carlos Fernandez, Foster J. Provost, Xintian Han
2020	J	`jnl`	In memory of Tom Fawcett. Mach. Learn. Foster J. Provost
2020	J	`jnl`	Methods for Individual Treatment Assignment: An Application and Comparison for Playlist Generation. CoRR Carlos Fernandez, Foster J. Provost, Jesse Anderton, Benjamin A. Carterette, Praveen Chandar
2019	J	`jnl`	Counterfactual Explanation Algorithms for Behavioral and Textual Data. CoRR Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou
2019	C	`conf`	Counterfactual Explanations for Data-Driven Decisions. ICIS Carlos Fernandez, Foster J. Provost, Xintian Han
2019	J	`jnl`	Deep Learning on Big, Sparse, Behavioral Data. Big Data Sofie De Cnudde, Yanou Ramon, David Martens, Foster J. Provost
2019	J	`jnl`	Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data. Data Min. Knowl. Discov. Jessica Clark, Foster J. Provost
2018	B	`ed.`	5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018, Turin, Italy, October 1-3, 2018 DSAA Francesco Bonchi, Foster J. Provost, Tina Eliassi-Rad, Wei Wang, Ciro Cattuto, Rayid Ghani
2018	J	`jnl`	Data-Driven Investment Strategies for Peer-to-Peer Lending: A Case Study for Teaching Data Science. Big Data Maxime C. Cohen, C. Daniel Guetta, Kevin Jiao, Foster J. Provost
2018	A*	`conf`	Societal Impact of Data Science and Artificial Intelligence. KDD Foster J. Provost, James Hodson, Jeannette M. Wing, Qiang Yang, Jennifer Neville
2018	J	`jnl`	Wallenius Bayes. Mach. Learn. Enric Junqué de Fortuny, David Martens, Foster J. Provost
2017	J	`jnl`	Big Data, Data Science, and Civil Rights. CoRR Solon Barocas, Elizabeth Bradley, Vasant G. Honavar, Foster J. Provost
2017	J	`jnl`	Cost-Effective Quality Assurance in Crowd Labeling. Inf. Syst. Res. Jing Wang, Panagiotis G. Ipeirotis, Foster J. Provost
2017	J	`jnl`	Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals. Big Data Daizhuo Chen, Samuel P. Fraiberger, Robert Moakler, Foster J. Provost
2016	J	`jnl`	Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals. CoRR Daizhuo Chen, Samuel P. Fraiberger, Robert Moakler, Foster J. Provost
2016	J	`jnl`	Explaining Classification Models Built on High-Dimensional Sparse Data. CoRR Julie Moeyersoms, Brian Dalessandro, Foster J. Provost, David Martens
2016	J	`jnl`	Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics. MIS Q. David Martens, Foster J. Provost, Jessica Clark, Enric Junqué de Fortuny
2016	A	`conf`	The Predictive Power of Massive Data about our Fine-Grained Behavior. WSDM Foster J. Provost
2015	J	`jnl`	Beat the Machine: Challenging Humans to Find a Predictive Model's "Unknown Unknowns". ACM J. Data Inf. Qual. Joshua Attenberg, Panos Ipeirotis, Foster J. Provost
2015	J	`jnl`	Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies. Big Data Brian Dalessandro, Rod Hook, Claudia Perlich, Foster J. Provost
2015	J	`jnl`	Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design. Inf. Syst. Res. Foster J. Provost, David Martens, Alan Murray
2015	—	`conf`	Iteratively refining SVMs using priors. IEEE BigData Enric Junqué de Fortuny, Theodoros Evgeniou, David Martens, Foster J. Provost
2015	A*	`conf`	Measuring Causal Impact of Online Actions via Natural Experiments: Application to Display Advertising. KDD Daniel N. Hill, Robert Moakler, Alan E. Hubbard, Vadim Tsemekhman, Foster J. Provost, Kiril Tsemekhman
2014	J	`jnl`	A Data Scientist's Guide to Start-Ups. Big Data Foster J. Provost, Geoffrey I. Webb, Ron Bekkerman, Oren Etzioni, Usama M. Fayyad, Claudia Perlich
2014	J	`jnl`	ACM SIGKDD 2014 to be Held August 24-27 in Manhattan. Big Data Foster J. Provost
2014	J	`jnl`	Authors' Response to Gong's, "Comment on Data Science and its Relationship to Big Data and Data-Driven Decision Making". Big Data Foster J. Provost, Tom Fawcett
2014	A*	`conf`	Corporate residence fraud detection. KDD Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster J. Provost, David Martens
2014	J	`jnl`	Explaining Data-Driven Document Classifications. MIS Q. David Martens, Foster J. Provost
2014	J	`jnl`	Machine learning for targeted display advertising: transfer learning in action. Mach. Learn. Claudia Perlich, Brian Dalessandro, Troy Raeder, Ori Stitelman, Foster J. Provost
2014	—	`conf`	Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground truth. ADKDD@KDD Melinda Han Williams, Claudia Perlich, Brian Dalessandro, Foster J. Provost
2014	J	`jnl`	Repeated labeling using multiple noisy labelers. Data Min. Knowl. Discov. Panagiotis G. Ipeirotis, Foster J. Provost, Victor S. Sheng, Jing Wang
2014	A*	`conf`	Scalable hands-free transfer learning for online advertising. KDD Brian Dalessandro, Daizhuo Chen, Troy Raeder, Claudia Perlich, Melinda Han Williams, Foster J. Provost
2013	J	`jnl`	Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data Foster J. Provost, Tom Fawcett
2013	—	`conf`	Hyperlocal: inferring location of IP addresses in real-time bid requests for mobile ads. LBSN Long T. Le, Tina Eliassi-Rad, Foster J. Provost, Lauren Moores
2013	A*	`conf`	Panel: a data scientist's guide to making money from start-ups. KDD Foster J. Provost, Geoffrey I. Webb
2013	J	`jnl`	Predictive Modeling With Big Data: Big Data Enric Junqué de Fortuny, David Martens, Foster J. Provost
2013	J	`jnl`	Research Commentary - Information in Digital, Economic, and Social Networks. Inf. Syst. Res. Arun Sundararajan, Foster J. Provost, Gal Oestreicher-Singer, Sinan Aral
2013	A*	`conf`	Scalable supervised dimensionality reduction using clustering. KDD Troy Raeder, Claudia Perlich, Brian Dalessandro, Ori Stitelman, Foster J. Provost
2013	A*	`conf`	Using co-visitation networks for detecting large scale online display advertising exchange fraud. KDD Ori Stitelman, Claudia Perlich, Brian Dalessandro, Rod Hook, Troy Raeder, Foster J. Provost
2012	A*	`conf`	Bid optimizing and inventory scoring in targeted online advertising. KDD Claudia Perlich, Brian Dalessandro, Rod Hook, Ori Stitelman, Troy Raeder, Foster J. Provost
2012	—	`conf`	Causally motivated attribution for online advertising. AdKDD@KDD Brian Dalessandro, Claudia Perlich, Ori Stitelman, Foster J. Provost
2012	A*	`conf`	Design principles of massive, robust prediction systems. KDD Troy Raeder, Ori Stitelman, Brian Dalessandro, Claudia Perlich, Foster J. Provost
2011	—	`conf`	Beat the Machine: Challenging Workers to Find the Unknown Unknowns. Human Computation Josh Attenberg, Panagiotis G. Ipeirotis, Foster J. Provost
2011	J	`jnl`	Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction CoRR Foster J. Provost, Gary M. Weiss
2011	A*	`conf`	Online active inference and learning. KDD Josh Attenberg, Foster J. Provost
2010	—	`conf`	A Unified Approach to Active Dual Supervision for Labeling Features and Examples. ECML/PKDD (1) Josh Attenberg, Prem Melville, Foster J. Provost
2010	J	`jnl`	Inactive learning?: difficulties employing active learning in practice. SIGKDD Explor. Josh Attenberg, Foster J. Provost
2010	—	`ed.`	Proceedings of the First Workshop on Social Media Analytics, SOMA@KDD 2010, Washington, DC, USA, July 25, 2010 SOMA@KDD Prem Melville, Jure Leskovec, Foster J. Provost
2010	—	`conf`	Quality management on Amazon Mechanical Turk. HCOMP@KDD Panagiotis G. Ipeirotis, Foster J. Provost, Jing Wang
2010	A*	`conf`	Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance. KDD Josh Attenberg, Foster J. Provost
2009	J	`jnl`	Active Feature-Value Acquisition. Manag. Sci. Maytal Saar-Tsechansky, Prem Melville, Foster J. Provost
2009	A*	`conf`	Audience selection for on-line brand advertising: privacy-friendly social network targeting. KDD Foster J. Provost, Brian Dalessandro, Rod Hook, Xiaohan Zhang, Alan Murray
2009	—	`conf`	Brand advertising, on-line audiences, and social media: invited talk. KDD Workshop on Data Mining and Audience Intelligence for Advertising Foster J. Provost
2009	—	`ed.`	Proceedings of the ACM SIGKDD Workshop on Human Computation, Paris, France, June 28, 2009 KDD Workshop on Human Computation Paul N. Bennett, Raman Chandrasekar, Max Chickering, Panagiotis G. Ipeirotis, Edith Law, Anton Mityagin, Foster J. Provost, Luis von Ahn
2008	A*	`conf`	Get another label? improving data quality and data mining using multiple, noisy labelers. KDD Victor S. Sheng, Foster J. Provost, Panagiotis G. Ipeirotis
2007	J	`jnl`	Classification in Networked Data: A Toolkit and a Univariate Case Study. J. Mach. Learn. Res. Sofus A. Macskassy, Foster J. Provost
2007	C	`conf`	Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. ICEC Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky
2007	J	`jnl`	Decision-Centric Active Learning of Binary-Outcome Models. Inf. Syst. Res. Maytal Saar-Tsechansky, Foster J. Provost
2007	J	`jnl`	Handling Missing Values when Applying Classification Models. J. Mach. Learn. Res. Maytal Saar-Tsechansky, Foster J. Provost
2007	—	`conf`	Learning and Inference in Massive Social Networks. MLG Shawndra Hill, Foster J. Provost, Chris Volinsky
2007	A*	`conf`	Modeling complex networks for electronic commerce. EC Foster J. Provost, Arun Sundararajan
2006	—	`conf`	A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring. SNA@ICML Sofus Attila Macskassy, Foster J. Provost
2006	J	`jnl`	Distribution-based aggregation for relational learning with identifier attributes. Mach. Learn. Claudia Perlich, Foster J. Provost
2005	A*	`conf`	An Expected Utility Approach to Active Feature-Value Acquisition. ICDM Prem Melville, Foster J. Provost, Raymond J. Mooney
2005	A*	`conf`	ROC confidence bands: an empirical evaluation. ICML Sofus A. Macskassy, Foster J. Provost, Saharon Rosset
2005	J	`jnl`	Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification. IEEE Trans. Knowl. Data Eng. Abraham Bernstein, Foster J. Provost, Shawndra Hill
2004	A*	`conf`	Active Feature-Value Acquisition for Classifier Induction. ICDM Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney
2004	J	`jnl`	Active Sampling for Class Probability Estimation and Ranking. Mach. Learn. Maytal Saar-Tsechansky, Foster J. Provost
2004	—	`conf`	Confidence Bands for ROC Curves: Methods and an Empirical Study. ROCAI Sofus A. Macskassy, Foster J. Provost
2004	—	`conf`	Knowledge Discovery Using Concept-Class Taxonomies. Australian Conference on Artificial Intelligence Venkateswarlu Kolluri, Foster J. Provost, Bruce G. Buchanan, Douglas Metzler
2003	A*	`conf`	Aggregation-based feature invention and relational concept classes. KDD Claudia Perlich, Foster J. Provost
2003	J	`jnl`	Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction. J. Artif. Intell. Res. Gary M. Weiss, Foster J. Provost
2003	J	`jnl`	Predicting citation rates for physics papers: constructing features for an ordered probit model. SIGKDD Explor. Claudia Perlich, Foster J. Provost, Sofus A. Macskassy
2003	J	`jnl`	The myth of the double-blind review?: author identification using only citations. SIGKDD Explor. Shawndra Hill, Foster J. Provost
2003	J	`jnl`	Tree Induction for Probability-Based Ranking. Mach. Learn. Foster J. Provost, Pedro M. Domingos
2003	J	`jnl`	Tree Induction vs. Logistic Regression: A Learning-Curve Analysis. J. Mach. Learn. Res. Claudia Perlich, Foster J. Provost, Jeffrey S. Simonoff
2001	A*	`conf`	Active Learning for Class Probability Estimation and Ranking. IJCAI Maytal Saar-Tsechansky, Foster J. Provost
2001	J	`jnl`	Applications of Data Mining to Electronic Commerce. Data Min. Knowl. Discov. Ron Kohavi, Foster J. Provost
2001	A*	`conf`	Intelligent Information Triage. SIGIR Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar
2001	A*	`ed.`	Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, San Francisco, CA, USA, August 26-29, 2001 KDD Doheon Lee, Mario Schkolnick, Foster J. Provost, Ramakrishnan Srikant
2001	J	`jnl`	Robust Classification for Imprecise Environments. Mach. Learn. Foster J. Provost, Tom Fawcett
2000	J	`jnl`	Applications of Data Mining to Electronic Commerce CoRR Ron Kohavi, Foster J. Provost
2000	J	`jnl`	Discovering Interesting Patterns for Investment Decision Making with GLOWER - A Genetic Learner Overlaid with Entropy Reduction. Data Min. Knowl. Discov. Vasant Dhar, Dashin Chou, Foster J. Provost
2000	J	`jnl`	Robust Classification for Imprecise Environments CoRR Foster J. Provost, Tom Fawcett
1999	J	`jnl`	A Survey of Methods for Scaling Up Inductive Algorithms. Data Min. Knowl. Discov. Foster J. Provost, Venkateswarlu Kolluri
1999	A*	`conf`	Activity Monitoring: Noticing Interesting Changes in Behavior. KDD Tom Fawcett, Foster J. Provost
1999	A*	`conf`	Efficient Progressive Sampling. KDD Foster J. Provost, David D. Jensen, Tim Oates
1999	J	`jnl`	Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study. Informatica (Slovenia) Foster J. Provost, Andrea Pohoreckyj Danyluk
1998	J	`jnl`	AI Approaches to Fraud Detection and Risk Management. AI Mag. Tom Fawcett, Ira J. Haimowitz, Foster J. Provost, Salvatore J. Stolfo
1998	J	`jnl`	Guest Editors' Introduction: On Applied Research in Machine Learning. Mach. Learn. Foster J. Provost, Ron Kohavi
1998	—	`conf`	Robust Classification Systems for Imprecise Environments. AAAI/IAAI Foster J. Provost, Tom Fawcett
1998	A*	`conf`	The Case against Accuracy Estimation for Comparing Induction Algorithms. ICML Foster J. Provost, Tom Fawcett, Ron Kohavi
1997	J	`jnl`	Adaptive Fraud Detection. Data Min. Knowl. Discov. Tom Fawcett, Foster J. Provost
1997	A*	`conf`	Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. KDD Foster J. Provost, Tom Fawcett
1997	A*	`conf`	Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation. KDD John M. Aronis, Foster J. Provost
1997	A*	`conf`	Scaling Up Inductive Algorithms: An Overview. KDD Foster J. Provost, Venkateswarlu Kolluri
1996	A*	`conf`	Combining Data Mining and Machine Learning for Effective User Profiling. KDD Tom Fawcett, Foster J. Provost
1996	A*	`conf`	Exploiting Background Knowledge in Automated Discovery. KDD John M. Aronis, Foster J. Provost, Bruce G. Buchanan
1996	J	`jnl`	Scaling Up Inductive Learning with Massive Parallelism. Mach. Learn. Foster J. Provost, John M. Aronis
1996	—	`conf`	Scaling Up: Distributed Machine Learning with Cooperation. AAAI/IAAI, Vol. 1 Foster J. Provost, Daniel N. Hennessy
1995	J	`jnl`	Inductive Policy: The Pragmatics of Bias Selection. Mach. Learn. Foster J. Provost, Bruce G. Buchanan
1994	A	`conf`	Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism. ISMB Foster J. Provost, Daniel N. Hennessy
1994	—	`conf`	Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning. KDD Workshop John M. Aronis, Foster J. Provost
1993	A*	`conf`	Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias. AAAI Foster J. Provost
1993	A*	`conf`	Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network. ICML Andrea Pohoreckyj Danyluk, Foster J. Provost
1992	J	`jnl`	A Distributed Algorithm for Embedding Trees in Hypercubes with Modifications for Run-Time Fault Tolerance. J. Parallel Distributed Comput. Foster J. Provost, Rami G. Melhem
1992	B	`conf`	ClimBS: Searching the Bias Space. ICTAI Foster J. Provost
1992	A*	`conf`	Inductive Policy. AAAI Foster J. Provost, Bruce G. Buchanan
1992	—	`conf`	Inductive Strengthening: the Effects of a Simple Heuristic for Restricting Hypothesis Space Search. AII Foster J. Provost, Bruce G. Buchanan
1990	—	`conf`	RL4: a tool for knowledge-based induction. TAI Scott H. Clearwater, Foster J. Provost