David Madigan's Papers

A Bayesian Feature Selection Score based on Naive Bayes models: Susana Eyheramendy and David Madigan, 2007.
Sequential Decision Making Algorithms for Port of Entry Inspection: Overcoming Computational Challenges: David Madigan, Sushil Mittal, and Fred Roberts, 2007.
Finding Predictive Runs with LAPS: Suhrid Balakrishnan and David Madigan, 2007.
Decision Trees for Functional Variables: Suhrid Balakrishnan and David Madigan, 2006.
Constructing Informative Prior Distributions from Domain Knowledge in Text Classification: Aynur Dayanik, David D. Lewis, David Madigan, Vladimir Menkov, and Alexander Genkin, 2006.
Experimental Analysis of Sequential Decision Making Algorithms for Port of Entry Inspection Procedures: Saket Anand, David Madigan, Richard Mammone, Saumitr Pathak and Fred Roberts, 2006.
Algorithms for Sparse Linear Classifiers in the Massive Data Setting: Suhrid Balakrishnan and David Madigan, 2007.
Bayesian Multinomial Logistic Regression for Author Identification: David Madigan, Alexander Genkin, David D. Lewis, and Dmitriy Fradkin, 2005.
Author Identification on the Large Scale: David Madigan, Alexander Genkin, David D. Lewis, Shlomo Argamon, Dmitriy Fradkin, and Li Ye. 2005.
A Novel Feature Selection Score for Text Categorization: Susana Eyheramendy and David Madigan, 2004.
A Flexible Bayesian Generalized Linear Model for Dichotomous Response Data with an Application to Text Categorization: Susana Eyheramendy and David Madigan, 2004.
Location Estimation in Wireless Networks: A Bayesian Approach (PDF): David Madigan, Wen-Hua Ju, P. Krishnan, A.S. Krishnakumar, 2004.
Large-Scale Bayesian Logistic Regression for Text Categorization: Alexander Genkin, David D. Lewis, and David Madigan, 2004.
Software
A One-Pass Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets: Suhrid Balakrishnan and David Madigan, 2004.
Presentation
Software
Bayesian Data Mining for Health Surveillance: David Madigan, 2004
To appear in Spatial and Syndromic Surveillance for Public Health, Andrew Lawson and Ken Kleinman, Editors.
Statistics and the War on Spam: David Madigan, 2004 (this is an earlier version of an essay for the new edition of Statistics, A Guide to the Unknown).
Probabilistic Temporal Reasoning: Steve Hanks and David Madigan, 2004
To appear in Handbook of Temporal Reasoning in Artificial Intelligence.
Discussion of "Least Angle Regression" by Efron, Johnstone, Hastie, and Tibshirani: David Madigan and Greg Ridgeway, 2003, Annals of Statistics.
Experiments with Random Projections for Machine Learning: Dmitriy Fradkin and David Madigan, 2003, KDD-03. (shorter version)
Sparse Bayesian Classifiers for Text Categorization: Susana Eyheramendy, Alexander Genkin, Wen-Hua Ju, David D. Lewis, and David Madigan, 2003.
Extreme Value Theory Applied to Document Retrieval from Large Collections: David Madigan, Yehuda Vardi, and Ishay Weissman, 2003, IR.
A Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets: Greg Ridgeway and David Madigan, 2002, JDMKD, to appear.
On Bayesian Learning of Sparse Classifiers (PDF): Wen-Hua Ju, David madigan, and Steven Scott, 2002.
On the Naive Bayes Model for Text Categorization (PDF): Susana Eyheramendy, David D. Lewis, and David Madigan, 2002, AISTATS-03.
On Retrieval Properties of Samples of Large Collections (PDF): David Madigan, Yehuda Vardi, and Ishay Weissman, 2002, AISTATS-03.
Bayesian Analysis of Massive Datasets via Particle Filters: Greg Ridgeway and David Madigan, 2002, KDD-02.
Bayesian Data Analysis for Data Mining (.gz) (PDF): David Madigan and Greg Ridgeway, 2002, Handbook of Data Mining.
Likelihood-based Data Squashing: A Modeling Approach to Instance Construction.: David Madigan, Nandini Raghavan, William DuMouchel, Martha Nason, Christian Posse, and Greg Ridgeway, 2000, JKDDM.
Separation and Completeness Properties for AMP Chain Graph Markov Models.: Michael Levitz, Michael D. Perlman, and David Madigan, 2000, Annals of Statistics.
Bayesian Model Averaging - A Tutorial (with discussion).: Jennifer Hoeting, David Madigan, Adrian E. Raftery, and Chris T. Volinsky, 1999, Statistical Science,14, 382--417.
The printed version has mucho typesetting problems - please use this web version.
Alternative Markov Properties for Chain Graphs.: Steen Andersson, David Madigan, and Michael Perlman, 1999, Scandinavian Journal of Statistics.
Bayesian Mixed-Effects Models for Recommender Systems.: Michelle Condliff, David Madigan, David D. Lewis, and Christian Posse, 1999, AISTATS-99.
Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model .: David Madigan, 1999, AISTATS-99.
Relaxing the local independence assumption for quantitative learning in acyclic directed graphical models.: Daniella Golinelli, David Madigan, and Guido Consonni, 1999, AISTATS-99.
Boosting Methodology for Regression Problems.: Greg Ridgeway, David Madigan, and Thomas Richardson, 1999, KDD-99.
Madigan Home Page

Papers and Technical Reports