Thursday January 4: tutorials

10.00 -- 12.00
David Edwards (Novo Pharmaceutical Company)
"Graphical models for mixed data : an introduction to the theory and practice"

2.00 -- 4.00
Yoav Freund (AT&T Research)
"Predicting More -- Assuming Less, new methodologies in statistical inference"

Coffee Break

4.30 -- 6.30
David MacKay (University of Cambridge)
"Error correcting codes and belief propagation"

Friday January 5

7.30 -- 8.45 Breakfast

8.45 -- 9.00 Opening

Morning 1: Clustering

9.00 -- 9.30
The Learning Curve Method Applied to Clustering
Chris Meek, Bo Thiesson, David Heckerman

9.30 -- 10.00
Bootstrapping Self-Organizing Maps to Assess the Statistical Significance of Local Proximity
Eric de Bodt, Marie Cottrell

10.00 -- 10.30
A Random Walks View of Spectral Segmentation
Marina Meila, Jianbo Shi

Coffee break

Morning 2: Speech/language

11.00 -- 11.30
Distributional Similarity, Frequency, and the Skew Divergence
Lillian Lee

11.30 -- 12.00
Handling Missing and Unreliable Information in Speech Recognition
Phil Green, Martin Cooke, Jon Barker, Ljubomir Josifovski

12.00 -- 2.00 Conference lunch

Afternoon: Graphical Model search

2.00 -- 2.30
Finding an optimal chain is harder than finding an optimal tree
Chris Meek

2.30 -- 3.00
An Anytime Algorithm for Causal Inference
Peter Spirtes

3.00 -- 3.30
A Simulation Study of Three Related Causal Data Mining Algorithms
Subramani Mani, Gregory Cooper

Coffee break

4.00 -- 5.00 Poster preview

Dinner (not provided)

7.30 -- 10.00 Poster session 1

Saturday January 6

7.30 -- 9.00 Breakfast

Morning 1: Boosting

9.00 -- 9.30
Boosting for Regression and Classification: Some Views from Analogy
Wenxin Jiang

9.30 -- 10.00
Online Bagging and Boosting
Nikunj Oza, Stuart Russell

Coffee break

Morning 2: Kernel methods

10.30 -- 11.00
An improved training algorithm for kernel Fisher discriminants
Sebastian Mika, Alexander Smola, Bernhard Schoelkopf

11.00 -- 11.30
A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds
Michael Tipping, Bernhard Schoelkopf

11.30 -- 12.30 Poster preview

Lunch (not provided)

5.00 -- 7.30 Poster session 2

7.30 -- 10.00 Conference banquet

Sunday, January 7

7.30 -- 9.00 Breakfast

Morning 1: Dynamic Bayesian networks

9.00 -- 9.30
Products of Hidden Markov Models
Andrew Brown, Geoffrey Hinton

9.30 -- 10.00
Solving Hidden-Mode Markov Decision Problems
Samuel Choi, Nevin Zhang, Dit-Yan Yeung

10.00 -- 10.30
Can the Computer Learn to Play Music Expressively?
Christopher Raphael

Coffee break

Morning 2: Multiple Models

11.00 -- 11.30
Hyperparameters for Soft Bayesian Model Selection
Adrian Corduneanu, Christopher Bishop

11.30 -- 12.00
Managing Multiple Models
Hugh Chipman, Edward George, Robert McCulloch

12.00 -- 12.15 Closing

Papers in poster session 1 (Friday January 5)

Image Decomposition and Tracking using Dynamic Positional Trees
Amos Storkey, Christopher Williams

Profile Likelihood in Directed Graphical Models from BUGS Output
Malene Hojbjerre

Stochastic System Monitoring and Control
Gregory Provan

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

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

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

Discriminant Analysis on Dissimilarity Data : a New Fast Gaussian like Algorithm
Guerin-Dugue Anne, Celeux Gilles

Bayesian Support Vector Regression
Martin Law, James Kwok

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

Dual perturb and combine algorithm
Pierre Geurts

Bagging and the Bayesian Bootstrap
Merlise Clyde, Herbert Lee

On the correspondence between partially-observable Markov decision processes and Bayes-adaptive Markov decision processes
Michael Duff

Learning mixtures of smooth nonuniform deformation fields for probabilistic image matching
Nebojsa Jojic, Brendan Frey, Patrice Simard, David Heckerman

Clustering in high dimensions: modular mixture models
Hagai Attias

Papers in poster session 2 (Saturday January 6)

Learning in Bayesian networks with mixed variables
Susanne Bottcher

Instrumental Variable Estimation of Causal Influence without Linearity
Richard Scheines, Greg Cooper

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

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

Another look at sensitivity of Bayesian networks to imprecise probabilities
Oscar Kipersztok, Hai-Qin Wang

Some variations on variation independence.
Philip Dawid

On searching for optimal classifiers among Bayesian networks
Robert Cowell

Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks
J. M. Pena, I. Izarzugaza, J. A. Lozano, E. Aldasoro and P. Larranaga

Statistical Aspects of Stochastic Logic Programs
James Cussens

Temporal Matching under Uncertainty
Ahmed Tawfik, Greg Scott

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

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

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

Predicting with Variables Constructed from Univariate Temporal Sequences
Mehmet Kayaalp, Greg Cooper, Gilles Clermont