AI & Statistics 2005


AISTATS 2005 Schedule



Wednesday January 5:
6:30- Arrival and Evening Reception
Thursday January 6: 
9:15-9:30 Welcome
9:30-10:15 Invited Talk
Tom Minka
Some Intuitions About Message Passing
10:15-11:05 Session
Semiparametric Latent Factor Models
Yee Whye Teh, Matthias Seeger and Michael I. Jordan
Approximate Inference for Infinite Contingent Bayesian Networks
Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell,
Daniel L. Ong and Andrey Kolobov
11:05-11:30 Coffee Break
11:30-1:10 Session
A Graphical Model for Simultaneous Partitioning and Labeling
Philip J. Cowans and Martin Szummer
*Best Student Paper Award*
Generative Model for Layers of Appearance and Deformation
Anitha Kannan, Nebojsa Jojic and Brendan Frey
Loss Functions for Discriminative Training of Energy-Based Models
Yann LeCun and Fu Jie Huang
Learning Causally Linked Markov Random Fields
Geoffrey Hinton, Simon Osindero and Kejie Bao
1:10-8:00 Break
8:00-8:45 Invited Talk
Steffen Lauritzen
Identification and Separation of DNA Mixtures using
Peak Area Information
8:45-9:10 Session
Nonlinear Dimensionality Reduction by Semidefinite Programming
and Kernel Matrix Factorization
Kilian Weinberger, Benjamin Packer and Lawrence Saul
*Best Student Paper Award*
9:10-11:10 Poster Session 1 - (click for poster titles)
Friday January 7:
9:15-10:00 Invited Talk
Craig Boutilier
Regret-based Methods for Decision Making and Preference Elicitation
10:00-10:50 Session
Defensive Forecasting
Vladimir Vovk, Akimichi Takemura and Glenn Shafer
Kernel Constrained Covariance for Dependence Measurement
Arthur Gretton, Alex Smola, Olivier Bousquet, Ralf Herbrich,
Andrei Belitski, Mark Augath, Yusuke Murayama,
Jon Pauls, Bernhard Schölkopf and Nikos Logothetis
10:50-11:15 Coffee Break
11:15-12:30 Session
Kernel Methods for Missing Variables
Alex Smola, S. V. N. Vishwanathan and Thomas Hofmann
Regularized Spectral Learning
Marina Meila, Susan Shortreed and Liang Xu
Learning Spectral Graph Segmentation
Timothée Cour, Nicolas Gogin and Jianbo Shi
12:30-5:00 Break
5:00-6:15 Session
Distributed Latent Variable Models of Lexical Co-occurrences
John Blitzer
On the Path to an Ideal ROC Curve:
Considering Cost Asymmetry in Learning Classifiers
Francis Bach, Amir Globerson and Fernando Pereira
*Best Student Paper Award*
Estimating Class Membership Probabilities using Classifier Learners
John Langford and Bianca Zadrozny
6:15-8:00 Poster Session 2 - (click for poster titles)
8:00- Banquet
Saturday January 8:
9:15-10:00 Invited Talk
Tommi Jaakkola
Information, transfer, and semi-supervised learning
10:00-10:50 Session
Robust Higher Order Statistics
Max Welling
Fast Non-Parametric Bayesian Inference on Infinite Trees
Marcus Hutter
10:50-11:15 Coffee Break
11:15-12:30 Session
Poisson-Networks: A Model for Structured Poisson Processes
Shyamsundar Rajaram, Graepel Thore and Ralf Herbrich
Autonomy Identification for Bayesian Network Structure Learning
Raanan Yehezkel and Boaz Lerner
Restructuring Dynamic Causal Systems in Equilibrium
Denver Dash
12:30-8:00 Break
8:00-10:00 Poster Session 3 - (click for poster titles)
10:00-10:25 Session
Probability and Statistics in the Law
Philip Dawid
10:25-11:10 Invited Talk
Nir Friedman
Probabilistic Models for Identifying Regulation Networks:
From Qualitative to Quantitative Models
11:10 Close of AISTATS 2005