Probabilistic Modeling

Dirichlet-Enhanced Spam Filtering based on Biased Samples NeurIPS 2006 Distributed Inference in Dynamical Systems NeurIPS 2006 Efficient Structure Learning of Markov Networks using L_1-Regularization NeurIPS 2006 Generalized Bradley-Terry Models and Multi-Class Probability Estimates JMLR 2006 Hierarchical Dirichlet Processes with Random Effects NeurIPS 2006 High-Dimensional Graphical Model Selection Using ℓ_1-Regularized Logistic Regression NeurIPS 2006 Inferring Network Structure from Co-Occurrences NeurIPS 2006 Information Bottleneck for Non Co-Occurrence Data NeurIPS 2006 Isotonic Conditional Random Fields and Local Sentiment Flow NeurIPS 2006 Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm NeurIPS 2006 Learning annotated hierarchies from relational data NeurIPS 2006 Learning Factor Graphs in Polynomial Time and Sample Complexity JMLR 2006 Learning Nonparametric Models for Probabilistic Imitation NeurIPS 2006 Learning Parts-Based Representations of Data JMLR 2006 Learning Structural Equation Models for fMRI NeurIPS 2006 Learning to be Bayesian without Supervision NeurIPS 2006 Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields NeurIPS 2006 Learning to parse images of articulated bodies NeurIPS 2006 Linear Programs for Hypotheses Selection in Probabilistic Inference Models JMLR 2006 Linear State-Space Models for Blind Source Separation JMLR 2006 Mixture Regression for Covariate Shift NeurIPS 2006 Modeling Dyadic Data with Binary Latent Factors NeurIPS 2006 Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model NeurIPS 2006 Modeling Human Motion Using Binary Latent Variables NeurIPS 2006 Multi-dynamic Bayesian Networks NeurIPS 2006