AISTATS 2001

46 papers

A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds Michael E. Tipping, Bernhard Schölkopf

A Non-Parametric EM-Style Algorithm for Imputing Missing Values Rich Caruana

A Random Walks View of Spectral Segmentation Marina Meilă, Jianbo Shi

A Simulation Study of Three Related Causal Data Mining Algorithms Subramani Mani, Gregory F. Cooper

An Anytime Algorithm for Causal Inference Peter Spirtes

An Improved Training Algorithm for Kernel Fisher Discriminants Sebastian Mika, Alexander J. Smola, Bernhard Schölkopf

Another Look at Sensitivity of Bayesian Networks to Imprecise Probabilities Oscar Kipersztok, Haiqin Wang

Are They Really Neighbors? a Statistical Analysis of the SOM Algorithm Output Eric Bodt, Marie Cottrell, Michel Verleysen

Bagging and the Bayesian Bootstrap Merlise Clyde, Herbert Lee

Bayesian Support Vector Regression Martin H. C. Law, James Tin-Yau Kwok

Can the Computer Learn to Play Music Expressively? Christopher Raphael

Comparing Prequential Model Selection Criteria in Supervised Learning of Mixture Models Petri Kontkanen, Petri Myllymäki, Henry Tirri

Discriminant Analysis on Dissimilarity Data : A New Fast Gaussian like Algorithm Anne Guérin-Dugué, Gilles Celeux

Dual Perturb and Combine Algorithm Pierre Geurts

Dynamic Positional Trees for Structural Image Analysis Amos J. Storkey, Christopher K. I. Williams

Finding a Path Is Harder than Finding a Tree Christopher Meek

Geographical Clustering of Cancer Incidence by Means of Bayesian Networks and Conditional Gaussian Networks José M. Peña, I. Izarzugaza, José Antonio Lozano, E. Aldasoro, Pedro Larrañaga

Handling Missing and Unreliable Information in Speech Recognition Phil D. Green, Jon Barker, Martin Cooke, Ljubomir Josifovski

Hyperparameters for Soft Bayesian Model Selection Adrian Corduneanu, Christopher M. Bishop

Information-Theoretic Advisors in Invisible Chess Ariel E. Bud, David W. Albrecht, Ann E. Nicholson, Ingrid Zukerman

Is Regularization Unnecessary for Boosting? Wenxin Jiang

Learning Bayesian Networks with Mixed Variables Susanne Bottcher

Learning in High Dimensions: Modular Mixture Models Hagai Attias

Learning Mixtures of Smooth, Nonuniform Deformation Models for Probabilistic Image Matching Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman

Managing Multiple Models Hugh A. Chipman, Edward I. George, Robert E. McCulloch

Message Length as an Effective Ockham’s Razor in Decision Tree Induction Scott Needham, David L. Dowe

Models for Conditional Probability Tables in Educational Assessment Russell G. Almond, Lou DiBello, Frank Jenkins, Deniz Senturk, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan

Monte-Carlo Algorithms for the Improvement of Finite-State Stochastic Controllers: Application to Bayes-Adaptive Markov Decision Processes Michael O. Duff

On Parameter Priors for Discrete DAG Models Dmitry Rusakov, Dan Geiger

On Searching for Optimal Classifiers Among Bayesian Networks Robert G. Cowell

On the Effectiveness of the Skew Divergence for Statistical Language Analysis Lillian Lee

Online Bagging and Boosting Nikunj C. Oza, Stuart J. Russell

Piecewise Linear Instrumental Variable Estimation of Causal Influence Richard Scheines, Gregory F. Cooper, Changwon Yoo, Tianjiao Chu

Predicting with Variables Constructed from Temporal Sequences Mehmet Kayaalp, Gregory F. Cooper, Gilles Clermont

Products of Hidden Markov Models Andrew D. Brown, Geoffrey E. Hinton

Profile Likelihood in Directed Graphical Models from BUGS Output Malene Højbjerre

Solving Hidden-Mode Markov Decision Problems Samuel Ping-Man Choi, Nevin Lianwen Zhang, Dit-Yan Yeung

Some Variations on Variation Independence. A. Philip Dawid

Statistical Aspects of Stochastic Logic Programs James Cussens

Stochastic System Monitoring and Control Gregory M. Provan

Temporal Matching Under Uncertainty Ahmed Y. Tawfik, Greg Scott

The Efficient Propagation of Arbitrary Subsets of Beliefs in Discrete-Valued Bayesian Networks Duncan Smith

The Learning Curve Method Applied to Clustering Christopher Meek, Bo Thiesson, David Heckerman

Using Unsupervised Learning to Guide Resampling in Imbalanced Data Sets Adam Nickerson, Nathalie Japkowicz, Evangelos E. Milios

Variational Learning for Multi-Layer Networks of Linear Threshold Units Neil D. Lawrence

Why Averaging Classifiers Can Protect Against Overfitting Yoav Freund, Yishay Mansour, Robert E. Schapire