Graphical Models

Learning on Graph with Laplacian Regularization NeurIPS 2006 Learning Structural Equation Models for fMRI NeurIPS 2006 Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields NeurIPS 2006 Learning with Hypergraphs: Clustering, Classification, and Embedding NeurIPS 2006 Linear Programming Relaxations and Belief Propagation -- An Empirical Study JMLR 2006 Linear State-Space Models for Blind Source Separation JMLR 2006 Manifold Denoising NeurIPS 2006 Modeling Human Motion Using Binary Latent Variables NeurIPS 2006 Statistical Modeling of Images with Fields of Gaussian Scale Mixtures NeurIPS 2006 Stochastic Relational Models for Discriminative Link Prediction NeurIPS 2006 Structured Prediction, Dual Extragradient and Bregman Projections JMLR 2006 Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks NeurIPS 2006 The Neurodynamics of Belief Propagation on Binary Markov Random Fields NeurIPS 2006 Training Conditional Random Fields for Maximum Labelwise Accuracy NeurIPS 2006 Using Combinatorial Optimization within Max-Product Belief Propagation NeurIPS 2006 Walk-Sums and Belief Propagation in Gaussian Graphical Models JMLR 2006 Local Propagation in Conditional Gaussian Bayesian Networks JMLR 2005 Optimal Structure Identification With Greedy Search JMLR 2002