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Graphical Models

132 directly classified papers

Papers per year

Papers

On Learning Discrete Graphical Models using Group-Sparse Regularization AISTATS 2011
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables AISTATS 2011
Kernel Embeddings of Latent Tree Graphical Models NIPS 2011
Uniqueness of Belief Propagation on Signed Graphs NIPS 2011
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions NIPS 2011
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation NIPS 2011
Extended Bayesian Information Criteria for Gaussian Graphical Models NIPS 2010
Graph-Valued Regression NIPS 2010
Efficient Relational Learning with Hidden Variable Detection NIPS 2010
Learning Efficient Markov Networks NIPS 2010
Implicit Differentiation by Perturbation NIPS 2010
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models NIPS 2010
Sparse Inverse Covariance Selection via Alternating Linearization Methods NIPS 2010
An LP View of the M-best MAP problem NIPS 2009
Which graphical models are difficult to learn? NIPS 2009
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data NIPS 2009
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation NIPS 2009
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models NIPS 2009
Sparse and Locally Constant Gaussian Graphical Models NIPS 2009
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE NIPS 2008
Learning Bounded Treewidth Bayesian Networks NIPS 2008
Efficient Exact Inference in Planar Ising Models NIPS 2008
Linear programming analysis of loopy belief propagation for weighted matching NIPS 2007
Cooled and Relaxed Survey Propagation for MRFs NIPS 2007
Hidden Common Cause Relations in Relational Learning NIPS 2007