Online Proceedings of The Seventh International Workshop on Artificial Intelligence and Statistics

January 3-6, Fort Lauderdale, Florida

Plenary Sessions

Attias	Hierarchical IFA belief networks
Brand	Pattern discovery via entropy minimization
Cox	Causal mechanisms and classification trees for predicting chemical carcinogens
Dawid Studeny	Conditional products: an alternative approach to conditional independence
Degeeter	Geometric modelling of a nuclear environment
Domingos	Process-oriented evaluation: The next step
Frey Fisher	Modeling decision tree performance with the power law
Friedman Nir Getoor	Learning structure from data efficiently: applying bounding techniques
Geiger Heckerman King Meek	On the geometry of DAG models with hidden variables
Gelfand Ghosh	Model choice
Haft Hofmann Tresp	Model-independent mean field theory as a local method for approximate propagation of information
Jaakkola Haussler	Probabilistic kernel regression models
Jiang Tanner	Hierarchical mixtures-of-experts for generalized linear models:some results on denseness and consistency
Kask Dechter	Stochastic local search for Bayesian network
Madigan	Bayesian graphical models for non-compliance in randomaized trials
Oates Cohen Durfee	Efficient mining of statistical dependencies
Richardson Bailer Banerjees	Efficient structure search in the presence of latent variables
Ridgeway Madigan Richardson	Boosting methodology for regression problems
Spirtes Cooper	An experiment in causal discovery using a pneumona database
Viswanathan	A note on the comparison of polynomial selection methods

Poster Sessions

Almond Herskovits Mislevey Steinberg	Transfer of information between system and evidence models
Chien George	Bayesian collaborative filtering
Cowell	Parameter learning from incomplete data for Bayesian networks
Friedman Goldszmidt Wyner	On the application of the bootstrap for computing confidence measures on features of induced Bayesian networks
Golinelli Madigan Consonni	Relaxing the local independence assumption for quantitative learning in acyclic directed graphical models through hierarchical partition models
Humphreys Titterington	A new method of learning in binary Boltzmann machines
Jensen	Statistical Challenges to inductive inference in linked data
Jorgensen Hunt	Mixture model clustering with the multimix program
Keogh Pazzani	Algorithms for learning augmented bayesian classifiers
Kontkanen Myllymaki Silander Tirri	Exploring the robustness of Bayesian and information-theoretic methods for predictive inference
Kreutz Reimetz Sendhoff Weihs Seelen	Structure optimization of density estimation models applied to regression problems with dynamic noise
Larkin	A learning rule based method of feature extraction with application to acoustic signal classification
Laskey	Learning extensible multi-entity directed graphical models
Monti Cooper	A latent variable model for multivariate discretization
Pearl Meshkat	Testing regression models with fewer regressors
Ramoni Sebastiani	Learning conditional probabilities from incomplete data: an experimental comparison
Rida Labbi Pelegrini	Experts combination through density decomposition
Roedder	Entropy driven probabilistic inference and inconsistency
Schmill Cohen	Learned models for continuous planning
Schubert	Efficient optimization of large k real-time control algorithm
Sebastiani Ramoni	Model folding for data subject to nonresponse
Settimi Smith	Geometry moments and Bayesian networks with hidden variables
Smyth	Joint probabilistic clustering of multivariate and sequential data
Stanghellini Whittaker	Analysis of multivariate time series via a hidden graphical model
Vogler	Visual design support for probabilistic network application