Graphical Models

Fast b-matching via Sufficient Selection Belief Propagation AISTATS 2011 Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference AISTATS 2011 Hidden-Unit Conditional Random Fields AISTATS 2011 High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions NeurIPS 2011 Inductive reasoning about chimeric creatures NeurIPS 2011 Inferring Interaction Networks using the IBP applied to microRNA Target Prediction NeurIPS 2011 Kernel Belief Propagation AISTATS 2011 Kernel Embeddings of Latent Tree Graphical Models NeurIPS 2011 Learning a Distance Metric from a Network NeurIPS 2011 Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables AISTATS 2011 Learning Higher-Order Graph Structure with Features by Structure Penalty NeurIPS 2011 Learning Scale Free Networks by Reweighted ℓ_1 regularization AISTATS 2011 Learning unbelievable probabilities NeurIPS 2011 Mapping Kernels Defined Over Countably Infinite Mapping Systems and their Application ACML 2011 Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning NeurIPS 2011 Message-Passing for Approximate MAP Inference with Latent Variables NeurIPS 2011 Mixed Cumulative Distribution Networks AISTATS 2011 Multicore Gibbs Sampling in Dense, Unstructured Graphs AISTATS 2011 Neural Reconstruction with Approximate Message Passing (NeuRAMP) NeurIPS 2011 On Learning Discrete Graphical Models using Greedy Methods NeurIPS 2011 On Learning Discrete Graphical Models using Group-Sparse Regularization AISTATS 2011 On Tracking The Partition Function NeurIPS 2011 Phase transition in the family of p-resistances NeurIPS 2011 Probabilistic Joint Image Segmentation and Labeling NeurIPS 2011 Regularized Laplacian Estimation and Fast Eigenvector Approximation NeurIPS 2011