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ORCID10th AISTATS 2005: Bridgetown, Barbados
- Robert G. Cowell, Zoubin Ghahramani:
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, AISTATS 2005, Bridgetown, Barbados, January 6-8, 2005. Society for Artificial Intelligence and Statistics 2005 - Shivani Agarwal, Sariel Har-Peled
, Dan Roth:
A Uniform Convergence Bound for the Area Under the ROC Curve. 1-8 - Francis R. Bach, David Heckerman, Eric Horvitz:
On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers. 9-16 - Misha Belkin, Partha Niyogi, Vikas Sindhwani:
On Manifold Regularization. 17-24 - John Blitzer, Amir Globerson, Fernando Pereira:
Distributed Latent Variable Models of Lexical Co-occurrences. 25-32 - Miguel Á. Carreira-Perpiñán, Geoffrey E. Hinton:
On Contrastive Divergence Learning. 33-40 - David Cavallini, Fabio Corradi:
OOBN for Forensic Identification through Searching a DNA profiles' Database. 41-48 - Olivier Chapelle:
Active Learning for Parzen Window Classifier. 49-56 - Olivier Chapelle, Alexander Zien:
Semi-Supervised Classification by Low Density Separation. 57-64 - Timothée Cour, Nicolas Gogin, Jianbo Shi:
Learning spectral graph segmentation. 65-72 - Philip J. Cowans, Martin Szummer:
A Graphical Model for Simultaneous Partitioning and Labeling. 73-80 - Denver Dash:
Restructuring Dynamic Causal Systems in Equilibrium. 81-88 - A. Philip Dawid:
Probability and Statistics in the Law. 89-95 - Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux:
Efficient Non-Parametric Function Induction in Semi-Supervised Learning. 96-103 - Dan Geiger, Christopher Meek:
Structured Variational Inference Procedures and their Realizations. 104-111 - Arthur Gretton, Alexander J. Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos K. Logothetis:
Kernel Constrained Covariance for Dependence Measurement. 112-119 - Jihun Ham, Daniel D. Lee, Lawrence K. Saul:
Semisupervised alignment of manifolds. 120-127 - Geoffrey E. Hinton, Simon Osindero, Kejie Bao:
Learning Causally Linked Markov Random Fields. 128-135 - Matthias Hein, Olivier Bousquet:
Hilbertian Metrics and Positive Definite Kernels on Probability Measures. 136-143 - Marcus Hutter:
Fast Non-Parametric Bayesian Inference on Infinite Trees. 144-151 - Søren Højsgaard, Steffen L. Lauritzen:
Restricted concentration models - graphical Gaussian models with concentration parameters restricted to being equal. 152-157 - Mike Klaas, Dustin Lang, Nando de Freitas:
Fast maximum a-posteriori inference on Monte Carlo state spaces. 158-165 - Anitha Kannan, Nebojsa Jojic, Brendan J. Frey:
Generative Model for Layers of Appearance and Deformation. 166-173 - Kevin H. Knuth:
Toward Question-Asking Machines: The Logic of Questions and the Inquiry Calculus. 174-180 - Vladimir Kolmogorov:
Convergent tree-reweighted message passing for energy minimization. 182-189 - Manabu Kuroki, Zhihong Cai:
Instrumental variable tests for Directed Acyclic Graph Models. 190-197 - John Langford, Bianca Zadrozny:
Estimating Class Membership Probabilities using Classifier Learners. 198-205 - Yann LeCun, Fu Jie Huang:
Loss Functions for Discriminative Training of Energy-Based Models. 206-213 - Florian Markowetz, Steffen Grossmann, Rainer Spang:
Probabilistic Soft Interventions in Conditional Gaussian Networks. 214-221 - Benjamin M. Marlin, Sam T. Roweis, Richard S. Zemel:
Unsupervised Learning with Non-Ignorable Missing Data. 222-229 - Marina Meila, Susan M. Shortreed, Liang Xu:
Regularized spectral learning. 230-237 - Brian Milch, Bhaskara Marthi, David A. Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov:
Approximate Inference for Infinite Contingent Bayesian Networks. 238-245 - Frederic Morin, Yoshua Bengio:
Hierarchical Probabilistic Neural Network Language Model. 246-252 - Marie Ouimet, Yoshua Bengio:
Greedy Spectral Embedding. 253-260 - John Platt:
FastMap, MetricMap, and Landmark MDS are all Nystrom Algorithms. 261-268 - Yuan (Alan) Qi, Martin Szummer, Tom Minka:
Bayesian Conditional Random Fields. 269-276 - Shyamsundar Rajaram, Thore Graepel, Ralf Herbrich:
Poisson-Networks: A Model for Structured Poisson Processes. 277-284 - Manuel Reyes-Gomez, Nebojsa Jojic, Daniel P. W. Ellis:
Deformable Spectrograms. 285-292 - Steven J. Rennie, Kannan Achan, Brendan J. Frey, Parham Aarabi:
Variational Speech Separation of More Sources than Mixtures. 293-300 - Carsten Riggelsen, Ad Feelders:
Learning Bayesian Network Models from Incomplete Data using Importance Sampling. 301-308 - Teemu Roos, Petri Myllymäki, Henry Tirri:
On the Behavior of MDL Denoising. 309-316 - Rómer Rosales, Tommi S. Jaakkola:
Focused Inference. 317-324 - Alexander J. Smola, S. V. N. Vishwanathan, Thomas Hofmann:
Kernel Methods for Missing Variables. 325-332 - Yee Whye Teh, Matthias W. Seeger, Michael I. Jordan:
Semiparametric latent factor models. 333-340 - Bo Thiesson, Christopher Meek:
Efficient Gradient Computation for Conditional Gaussian Models. 341-348 - Ivor W. Tsang, James Tin-Yau Kwok, Pak-Ming Cheung:
Very Large SVM Training using Core Vector Machines. 349-356 - Lyle H. Ungar, Jing Zhou, Dean P. Foster, Bob A. Stine:
Streaming Feature Selection using IIC. 357-364 - Vladimir Vovk, Akimichi Takemura, Glenn Shafer:
Defensive Forecasting. 365-372 - Bo Wang, D. M. Titterington:
Inadequacy of interval estimates corresponding to variational Bayesian approximations. 373-380 - Kilian Q. Weinberger, Benjamin Packer, Lawrence K. Saul:
Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization. 381-388 - Max Welling:
An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions. 389-396 - Max Welling, Charles Sutton:
Learning in Markov Random Fields with Contrastive Free Energies. 397-404 - Max Welling:
Robust Higher Order Statistics. 405-412 - Jason Weston, Antoine Bordes, Léon Bottou:
Online (and Offline) on an Even Tighter Budget. 413-420 - Wim Wiegerinck:
Approximations with Reweighted Generalized Belief Propagation. 421-428 - Raanan Yehezkel, Boaz Lerner:
Recursive Autonomy Identification for Bayesian Network Structure Learning. 429-436 - Kai Yu, Shipeng Yu, Volker Tresp:
Dirichlet Enhanced Latent Semantic Analysis. 437-444 - Onno Zoeter, Tom Heskes:
Gaussian Quadrature Based Expectation Propagation. 445-452
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