Structure Learning

Graphical continuous Lyapunov models UAI 2020 Learning decomposable models by coarsening PGM 2020 Structure Learning from Related Data Sets with a Hierarchical Bayesian Score PGM 2020 Structure Learning with Side Information: Sample Complexity NeurIPS 2019 The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks JMLR 2019 Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data NeurIPS 2018 Intervals of Causal Effects for Learning Causal Graphical Models PGM 2018 Learning Bayesian Networks by Branching on Constraints PGM 2018 Who Learns Better Bayesian Network Structures: Constraint-Based, Score-based or Hybrid Algorithms? PGM 2018 Learning Bayesian networks with ancestral constraints NeurIPS 2016 Learning Bayesian Networks with Thousands of Variables NeurIPS 2015 Rate-Agnostic (Causal) Structure Learning NeurIPS 2015 Advances in Learning Bayesian Networks of Bounded Treewidth NeurIPS 2014 Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video CVPR 2014 Learning Chordal Markov Networks by Dynamic Programming NeurIPS 2014 Structure learning of antiferromagnetic Ising models NeurIPS 2014 Learning Chordal Markov Networks by Constraint Satisfaction NeurIPS 2013 On Poisson Graphical Models NeurIPS 2013 Tracking Time-varying Graphical Structure NeurIPS 2013 Unsupervised Structure Learning of Stochastic And-Or Grammars NeurIPS 2013 Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs NeurIPS 2012 Learning Networks of Heterogeneous Influence NeurIPS 2012 Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses NeurIPS 2012 A Global Structural EM Algorithm for a Model of Cancer Progression NeurIPS 2011 Confidence Sets for Network Structure NeurIPS 2011