Foster J. Provost

Foster J. Provost, Scott H. Clearwater, S. Clearwater, 1990, [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence.

Intelligent information triage

Vasant Dhar, Haym Hirsh, Foster J. Provost, 2001, SIGIR '01.

Discovering Interesting Patterns for Investment Decision Making with GLOWER ☹—A Genetic Learner Overlaid with Entropy Reduction

Vasant Dhar, Foster J. Provost, Dashin Chou, 2000, Data Mining and Knowledge Discovery.

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

Foster J. Provost, John M. Aronis, F. Provost, 1997, KDD.

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

Panagiotis G. Ipeirotis, Victor S. Sheng, Foster J. Provost, 2008, KDD.

Active Learning for Class Probability Estimation and Ranking

Foster J. Provost, Maytal Saar-Tsechansky, 2001, IJCAI.

Scalable hands-free transfer learning for online advertising

Foster J. Provost, Troy Raeder, Brian Dalessandro, 2014, KDD.

Bid optimizing and inventory scoring in targeted online advertising

Foster J. Provost, Troy Raeder, Ori Stitelman, 2012, KDD.

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

Foster J. Provost, Andrea Pohoreckyj Danyluk, F. Provost, 1993, ICML.

Handling Missing Values when Applying Classification Models

Foster J. Provost, Maytal Saar-Tsechansky, F. Provost, 2007, J. Mach. Learn. Res..

Active Feature-Value Acquisition

Foster J. Provost, Maytal Saar-Tsechansky, Prem Melville, 2009, Manag. Sci..

Cost-Effective Quality Assurance in Crowd Labeling

Jing Wang, Panagiotis G. Ipeirotis, Foster J. Provost, 2016, Inf. Syst. Res..

Confidence Bands for Roc Curves

Foster J. Provost, Sofus A. Macskassy, F. Provost, 2004, ROCAI.

Efficient progressive sampling

Tim Oates, Foster J. Provost, David D. Jensen, 1999, KDD '99.

Decision-Centric Active Learning of Binary-Outcome Models

Foster J. Provost, Maytal Saar-Tsechansky, F. Provost, 2007, Inf. Syst. Res..

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

Foster J. Provost, Samuel P. Fraiberger, Daizhuo Chen, 2016, Big Data.

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

Foster J. Provost, Maytal Saar-Tsechansky, Prem Melville, 2007, ICEC.

Robust Classification Systems for Imprecise Environments

Tom Fawcett, Foster J. Provost, Tom Fawcett, 1998, AAAI/IAAI.

Applications of Data Mining to Electronic Commerce

Ron Kohavi, Foster J. Provost, Ron Kohavi, 2000, Springer US.

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

Geoffrey I. Webb, Foster J. Provost, 2013, KDD.

Scaling Up: Distributed Machine Learning with Cooperation

Foster J. Provost, Daniel N. Hennessy, 1996, AAAI/IAAI, Vol. 1.

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

Foster J. Provost, Bruce G. Buchanan, 1992, AII.

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

Tina Eliassi-Rad, Foster J. Provost, Long T. Le, 2013, LBSN '13.

Societal Impact of Data Science and Artificial Intelligence

Jennifer Neville, Foster J. Provost, Qiang Yang, 2018, KDD.

Robust Classification for Imprecise Environments

Tom Fawcett, Foster J. Provost, Tom Fawcett, 2000, Machine Learning.

Corporate residence fraud detection

Foster J. Provost, David Martens, Enric Junqué de Fortuny, 2014, KDD.

Knowledge Discovery Using Concept-Class Taxonomies

Foster J. Provost, Bruce G. Buchanan, Venkateswarlu Kolluri, 2004, Australian Conference on Artificial Intelligence.

Active Sampling for Class Probability Estimation and Ranking

Foster J. Provost, Maytal Saar-Tsechansky, F. Provost, 2004, Machine Learning.

Alan Murray, Foster J. Provost, David Martens, 2015, Inf. Syst. Res..

Modeling complex networks for electronic commerce

Arun Sundararajan, Foster J. Provost, F. Provost, 2007, EC '07.

Scalable supervised dimensionality reduction using clustering

Foster J. Provost, Troy Raeder, Ori Stitelman, 2013, KDD.

Combining Data Mining and Machine Learning for Effective User Profiling

Tom Fawcett, Foster J. Provost, Tom Fawcett, 1996, KDD.

Explaining Classification Models Built on High-Dimensional Sparse Data pdf

Foster J. Provost, David Martens, Brian Dalessandro, 2016, ArXiv.

The Case against Accuracy Estimation for Comparing Induction Algorithms

Ron Kohavi, Tom Fawcett, Foster J. Provost, 1998, ICML.

Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism

Foster J. Provost, Daniel N. Hennessy, 1994, ISMB.

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

Foster J. Provost, Sofus A. Macskassy, Claudia Perlich, 2003, SKDD.

Tree Induction for Probability-Based Ranking

Pedro M. Domingos, Foster J. Provost, F. Provost, 2003, Machine Learning.

Machine learning for targeted display advertising: transfer learning in action

Foster J. Provost, Troy Raeder, Ori Stitelman, 2013, Machine Learning.

Online active inference and learning

Foster J. Provost, Josh Attenberg, 2011, KDD.

Aggregation-based feature invention and relational concept classes

Foster J. Provost, Claudia Perlich, F. Provost, 2003, KDD '03.

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

Rami G. Melhem, Foster J. Provost, 1992, J. Parallel Distributed Comput..

Explaining Data-Driven Document Classifications

Foster J. Provost, David Martens, F. Provost, 2013, MIS Q..

A Data Scientist's Guide to Start-Ups

Oren Etzioni, Geoffrey I. Webb, Foster J. Provost, 2014, Big Data.

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

Foster J. Provost, Gary M. Weiss, F. Provost, 2003, J. Artif. Intell. Res..

ROC confidence bands: an empirical evaluation

Foster J. Provost, Saharon Rosset, Sofus A. Macskassy, 2005, ICML.

Applications of Data Mining to Electronic Commerce

Ron Kohavi, Foster J. Provost, Ron Kohavi, 2000, Data Mining and Knowledge Discovery.

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

Foster J. Provost, Sofus A. Macskassy, Sofus Attila Macskassy, 2006, SNA@ICML.

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

Foster J. Provost, 1993, AAAI.

Repeated labeling using multiple noisy labelers

Panagiotis G. Ipeirotis, Victor S. Sheng, Foster J. Provost, 2012, Data Mining and Knowledge Discovery.

Beat the Machine: Challenging Humans to Find a Predictive Model's “Unknown Unknowns”

Panagiotis G. Ipeirotis, Foster J. Provost, Josh Attenberg, 2015, JDIQ.

Toward intelligent assistance for a data mining process: an ontology-based approach for cost-sensitive classification

Abraham Bernstein, Foster J. Provost, Shawndra Hill, 2005, IEEE Transactions on Knowledge and Data Engineering.

Tree Induction Vs Logistic Regression: A Learning Curve Analysis

Jeffrey S. Simonoff, Foster J. Provost, Claudia Perlich, 2001, J. Mach. Learn. Res..

Guest Editors' Introduction: On Applied Research in Machine Learning

Ron Kohavi, Foster J. Provost, Ron Kohavi, 1998, Machine Learning.

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

Foster J. Provost, Alan E. Hubbard, Daniel N. Hill, 2015, KDD.

Exploiting Background Knowledge in Automated Discovery

Foster J. Provost, Bruce G. Buchanan, John M. Aronis, 1996, KDD.

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

Foster J. Provost, Josh Attenberg, F. Provost, 2010, KDD.

Distribution-based aggregation for relational learning with identifier attributes

Foster J. Provost, Claudia Perlich, F. Provost, 2006, Machine Learning.

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

Foster J. Provost, Sofus A. Macskassy, F. Provost, 2007, J. Mach. Learn. Res..

ClimBS: searching the bias space

Foster J. Provost, 1992, Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92.

Big Data, Data Science, and Civil Rights pdf

Solon Barocas, Vasant Honavar, Elizabeth Bradley, 2017, ArXiv.

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

Foster J. Provost, Troy Raeder, Ori Stitelman, 2013, KDD.

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

Foster J. Provost, Andrea Pohoreckyj Danyluk, 1999, Informatica.

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

Tom Fawcett, Foster J. Provost, Tom Fawcett, 1997, KDD.

E ciently Constructing Relational Features from Background Knowledge for Inductive Machine Learning Also appears in Proceedings AAAI-94 Workshop on Knowledge Discovery in Databases

Foster J. Provost, John M. Aronis, 2007 .