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ORCID7th AISTATS 1999: Fort Lauderdale, Florida, USA
- David Heckerman, Joe Whittaker:
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, AISTATS 1999, Fort Lauderdale, Florida, USA, January 3-6, 1999. Society for Artificial Intelligence and Statistics 1999
Online Proceedings of The Seventh International Workshop on Artificial Intelligence and Statistics
Plenary Sessions
- Hagai Attias:
Hierarchical IFA Belief Networks. - Matthew Brand:
Pattern discovery via entropy minimization. - Louis Anthony Cox Jr.:
Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens. - A. Philip Dawid, Milan Studený:
Conditional products: An alternative approach to conditional independence. - Jan De Geeter, Marc Decréton, Joris De Schutter, Herman Bruyninckx, Hendrik Van Brussel:
Geometric modeling of a nuclear environment. - Pedro M. Domingos:
Process-oriented evaluation: The next step. - Lewis J. Frey, Douglas H. Fisher:
Modeling decision tree performance with the power law. - Nir Friedman, Lise Getoor:
Efficient learning using constrained sufficient statistics. - Dan Geiger, David Heckerman, Henry King, Christopher Meek:
On the geometry of DAG models with hidden variables. - Sujit Kumar Ghosh, Alan E. Gelfand:
Model choice: A minimum posterior predictive loss approach. - Michael Haft, Reimar Hofmann, Volker Tresp:
Mean field inference in a general probabilistic setting. - Tommi S. Jaakkola, David Haussler:
Probabilistic kernel regression models. - Wenxin Jiang, Martin A. Tanner:
Hierarchical mixtures-of-experts for generalized linear models: some results on denseness and consistency. - Kalev Kask, Rina Dechter:
Stochastic local search for Bayesian network. AISTATS 1999 - David Madigan:
Bayesian graphical models, intention-to-treat, and the rubin causal Model. - Tim Oates, Matthew D. Schmill, Paul R. Cohen, Casey Durfee:
Efficient mining of statistical dependencies. - Thomas Richardson, Heiko Bailer, Moulinath Banarjees:
Tractable structure search in the presence of latent variables. - Greg Ridgeway, David Madigan, Thomas Richardson:
Boosting methodology for regression problems. - Peter Spirtes, Gregory F. Cooper:
An experiment in causal discovery using a pneumona database. - Murlikrishna Viswanathan, Chris S. Wallace:
A note on the comparison of polynomial selection methods.
Poster Sessions
- Russell G. Almond, Edward Herskovits, Robert J. Mislevy, Linda Stienberg:
Transfer of information between system and evidence models. - Yung-Hsin Chien, Edward I. George:
A bayesian model for collaborative filtering. - Robert G. Cowell:
Parameter learning from incomplete data for Bayesian networks. AISTATS 1999 - Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner:
On the application of the bootstrap for computing confidence measures on features of induced Bayesian networks. - Daniela Golinelli, David Madigan, Guido Consonni:
Relaxing the local independence assumption for quantitative learning in acyclic directed graphical models through hierarchical partition models. - Keith Humphreys, D. M. Titterington:
The exploration of new methods for learning in binary Boltzmann machines. - David D. Jensen:
Statistical challenges to inductive inference in linked data. AISTATS 1999 - Murray A. Jorgensen, Lynette A. Hunt:
Mixture model clustering with the multimix program. - Eamonn J. Keogh, Michael J. Pazzani:
Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. - Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
Exploring the robustness of Bayesian and information-theoretic methods for predictive inference. AISTATS 1999 - Martin Kreutz, Anja M. Reimetz, Bernhard Sendhoff, Claus Weihs, Werner von Seelen:
Structure optimization of density estimation models applied to regression problems with dynamic noise. - Michael Larkin:
A learning rule based method of feature extraction with application to acoustic signal classification. AISTATS 1999 - Kathryn Blackmond Laskey:
Learning extensible multi-entity directed graphical models. - Stefano Monti, Gregory F. Cooper:
A latent variable model for multivariate discretization. - Judea Pearl, Peyman Meshkat:
Testing regression models with fewer regressors. - Marco Ramoni, Paola Sebastiani:
Learning conditional probabilities from incomplete databases - An experimental comparison. - Ahmed Rida, Abderrahim Labbi, Christian Pellegrini:
Local experts combination through density decomposition. - Wilhelm Rödder, Longgui Xu:
Entropy-driven inference and inconsistency. - Matthew D. Schmill, Tim Oates, Paul R. Cohen:
Learned models for continuous planning. - Peter J. Schubert, Daniel H. Loughlin:
Efficient optimization of large k real-time control algorithm. - Paola Sebastiani, Marco Ramoni:
Model folding for data subject to nonresponse. - Raffaella Settimi, Jim Q. Smith:
Geometry, moments and Bayesian networks with hidden variables. - Padhraic Smyth:
Joint probabilistic clustering of multivariate and sequential data. AISTATS 1999 - Elena Stanghellini, Joe Whittaker:
Analysis of multivariate time series via a hidden graphical model. - Axel Vogler:
Visual design support for probabilistic network application.
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