probabilistic graphical model

Methods for recovering conditional independence graphs (Abstract Reprint) IJCAI 2024 MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization AISTATS 2024 On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models NeurIPS 2024 PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX JMLR 2024 Qualitative Mechanism Independence NeurIPS 2024 Rethinking and Extending the Probabilistic Inference Capacity of GNNs ICLR 2024 Structural perspective on constraint-based learning of Markov networks AISTATS 2024 What type of inference is planning? NeurIPS 2024 Approximate inference of marginals using the IBIA framework NeurIPS 2023 Causal Effect Identification in Uncertain Causal Networks NeurIPS 2023 Computer Vision to the Rescue: Infant Postural Symmetry Estimation From Incongruent Annotations WACV 2023 Factor Graph Neural Networks JMLR 2023 Hierarchical clustering with dot products recovers hidden tree structure NeurIPS 2023 Inference for probabilistic dependency graphs UAI 2023 Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level AAAI 2023 Partial Multi-Label Learning with Probabilistic Graphical Disambiguation NeurIPS 2023 Rehearsal Learning for Avoiding Undesired Future NeurIPS 2023 Relevant Walk Search for Explaining Graph Neural Networks ICML 2023 Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge ICML 2023 Revisiting Structured Variational Autoencoders ICML 2023 Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms ICLR 2023 The d-Separation Criterion in Categorical Probability JMLR 2023 Anytime Learning of Sum-Product and Sum-Product-Max Networks PGM 2022 A Probabilistic Graphical Model Based on Neural-Symbolic Reasoning for Visual Relationship Detection CVPR 2022 Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling PGM 2022