AISTATS 2005

57 papers

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

A Uniform Convergence Bound for the Area Under the 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 L. 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 Maximum A-Posteriori Inference on Monte Carlo State Spaces Mike Klaas, Dustin Lang, Nando Freitas

Fast Non-Parametric Bayesian Inference on Infinite Trees Marcus Hutter

FastMap, MetricMap, and Landmark MDS Are All Nyström Algorithms John Platt

Focused Inference Romer Rosales, Tommi S. 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

Inadequacy of 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, Alexander Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos Logothetis

Kernel Methods for Missing Variables Alex J. Smola, S. V. N. Vishwanathan, 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 Timothée 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, Benjamin Packer, Lawrence Saul

On Contrastive Divergence Learning Miguel Á. Carreira-Perpiñán, 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

OOBN for Forensic Identification Through Searching a DNA Profiles’ Database David Cavallini, Fabio Corradi

Poisson-Networks: A Model for Structured Poisson Processes Shyamsundar Rajaram, Thore Graepel, 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 Meilă, Susan Shortreed, Liang Xu

Restricted Concentration Models – Graphical Gaussian Models with Concentration Parameters Restricted to Being Equal Søren Højsgaard, Steffen Lauritzen

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

Streaming Feature Selection Using IIC Lyle H. Ungar, Jing Zhou, Dean P. Foster, Bob A. 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 M. Marlin, Sam T. Roweis, Richard S. Zemel

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

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