Graphical Models

On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2019 OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models IJCAI 2019 Phrase Grounding by Soft-Label Chain Conditional Random Field EMNLP 2019 Random Clique Covers for Graphs with Local Density and Global Sparsity UAI 2019 Random Walk Decay Centrality AAAI 2019 Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching NeurIPS 2019 Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications AAAI 2019 Size of Interventional Markov Equivalence Classes in random DAG models AISTATS 2019 Smoothing Structured Decomposable Circuits NeurIPS 2019 Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models NeurIPS 2019 Structure Learning with Side Information: Sample Complexity NeurIPS 2019 The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks JMLR 2019 Towards Gene Function Prediction via Multi-Networks Representation Learning AAAI 2019 Video Relationship Reasoning Using Gated Spatio-Temporal Energy Graph CVPR 2019 Weakly Supervised Energy-Based Learning for Action Segmentation ICCV 2019 A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models ICML 2018 A Lattice Representation of Independence Relations PGM 2018 A Morphological Analyzer for Shipibo-Konibo EMNLP 2018 An Empirical Study of Methods for SPN Learning and Inference PGM 2018 A Novel Approach to Handle Inference in Discrete Markov Networks with Large Label Sets PGM 2018 A partial orthogonalization method for simulating covariance and concentration graph matrices PGM 2018 Approximating the maximum weighted decomposable graph problem with applications to probabilistic graphical models PGM 2018 A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices NeurIPS 2018 Bayesian Network Structure Learning with Side Constraints PGM 2018 Bayesian Semi-supervised Learning with Graph Gaussian Processes NeurIPS 2018