AI & Statistics 2005

List of Accepted Papers

A Graphical Model for Simultaneous Partitioning and Labeling
Philip Cowans, Martin Szummer

A Uniform Convergence Bound for the Area Under an ROC Curve
Shivani Agarwal, Sariel Har-Peled, Dan Roth

Active Learning for Parzen Window Classifier
Olivier Chapelle

An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions
Max Welling

Approximate Inference for Infinite Contingent Bayesian Networks
Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell, Daniel Ong, Andrey Kolobov

Approximations with Reweighted Generalized Belief Propagation
Wim Wiegerinck

Bayesian Conditional Random Fields
Yuan Qi, Martin Szummer, Tom Minka

Convergent tree-reweighted message passing for energy minimization
Vladimir Kolmogorov

Defensive Forecasting
Vladimir Vovk, Akimichi Takemura, Glenn Shafer

Deformable Spectrograms
Manuel Reyes-Gomez, Nebojsa Jojic, Daniel Ellis

Dirichlet Enhanced Latent Semantic Analysis
Kai Yu, Shipeng Yu, Volker Tresp

Distributed Latent Variable Models of Lexical Co-occurrences
John Blitzer, Amir Globerson, Fernando Pereira

Efficient Gradient Computation for Conditional Gaussian Models
Bo Thiesson, Chris Meek

Efficient Non-Parametric Function Induction in Semi-Supervised Learning
Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux

Estimating Class Membership Probabilities using Classifier Learners
John Langford, Bianca Zadrozny

Fast Non-Parametric Bayesian Inference on Infinite Trees
Marcus Hutter

Fast maximum a-posteriori inference on Monte Carlo state spaces
Mike Klaas, Dustin Lang, Nando de Freitas

FastMap, MetricMap, and Landmark MDS are all Nystrom Algorithms
John Platt

Focused Inference
Romer Rosales, Tommi Jaakkola

Gaussian Quadrature Based Expectation Propagation
Onno Zoeter, Tom Heskes

Generative Model for Layers of Appearance and Deformation
Anitha Kannan, Nebojsa Jojic, Brendan Frey

Greedy Spectral Embedding
Marie Ouimet, Yoshua Bengio

Hierarchical Probabilistic Neural Network Language Model
Frederic Morin, Yoshua Bengio

Hilbertian Metrics and Positive Definite Kernels on Probability Measures
Matthias Hein, Olivier Bousquet

Inadequate interval estimates corresponding to variational Bayesian approximations
Bo Wang, D.M. Titterington

Instrumental variable tests for Directed Acyclic Graph Models
Manabu Kuroki, Zhihong Cai

Kernel Constrained Covariance for Dependence Measurement
Arthur Gretton, Alex Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schoelkopf, Nikos Logothetis

Kernel Methods for Missing Variables
Alex Smola, Vishwanathan S V N, Thomas Hofmann

Learning Bayesian Network Models from Incomplete Data using Importance Sampling
Carsten Riggelsen, Ad Feelders

Learning Causally Linked Markov Random Fields
Geoffrey Hinton, Simon Osindero, Kejie Bao

Learning in Markov Random Fields with Contrastive Free Energies
Max Welling, Charles Sutton

Learning spectral graph segmentation
Timothee Cour, Nicolas Gogin, Jianbo Shi

Loss Functions for Discriminative Training of Energy-Based Models
Yann LeCun, Fu-Jie Huang

Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization
Kilian Weinberger, Lawrence Saul, Benjamin Packer

OOBN for Forensic Identification through Searching a DNA profiles' Database
David Cavallini, Fabio Corradi

On Contrastive Divergence Learning
Miguel Carreira-Perpinan, Geoffrey Hinton

On Manifold Regularization
Misha Belkin, Partha Niyogi, Vikas Sindhwani

On the Behavior of MDL Denoising
Teemu Roos, Petri Myllymäki, Henry Tirri

On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers
Francis Bach, David Heckerman, Eric Horvitz

Online (and Offline) on an Even Tighter Budget
Jason Weston, Antoine Bordes, Leon Bottou

Patterned graphical Gaussian models
Søren Højsgaard, Steffen Lauritzen

Poisson-Networks: A Model for Structured Poisson Processes
Shyamsundar Rajaram, Graepel Thore, Ralf Herbrich

Probabilistic Soft Interventions in Conditional Gaussian Networks
Florian Markowetz, Steffen Grossmann, Rainer Spang

Probability and Statistics in the Law
Philip Dawid

Recursive Autonomy Identification for Bayesian Network Structure Learning
Raanan Yehezkel, Boaz Lerner

Regularized spectral learning
Marina Meila, Susan Shortreed, Liang Xu

Restructuring Dynamic Causal Systems in Equilibrium
Denver Dash

Robust Higher Order Statistics
Max Welling

Semi-Supervised Classification by Low Density Separation
Olivier Chapelle, Alexander Zien

Semiparametric latent factor models
Yee Whye Teh, Matthias Seeger, Michael I. Jordan

Semisupervised alignment of manifolds
Jihun Ham, Daniel Lee, Lawrence Saul

Stacked Sequential Learning
William Cohen

Streaming Feature Selection using IIC
Lyle Ungar, Jing Zhou, Dean foster, Bob Stine

Structured Variational Inference Procedures and their Realizations
Dan Geiger, Chris Meek

Toward Question-Asking Machines: The Logic of Questions and the Inquiry Calculus
Kevin Knuth

Unsupervised Learning with Non-Ignorable Missing Data
Benjamin Marlin, Sam Roweis, Richard S. Zemel

Variational Speech Separation of More Sources than Mixtures
Steven Rennie, Kannan Achan, Brendan Frey, Parham Aarabi

Very Large SVM Training using Core Vector Machines
Ivor Tsang, James Kwok, Pak-Ming Cheung