Structure Learning

Causal Discovery with Metadata-Informed Latent Types UAI 2026 Don’t Test What You Can Deduce: Causal Discovery with Logical Inference UAI 2026 Learning Bayesian and Markov Networks with an Unreliable Oracle UAI 2026 Learning Ising Models from Evolutions (Extended Abstract) COLT 2026 Local Markov Equivalence for PC-style Local Causal Discovery and Identification of Controlled Direct Effects CLeaR 2026 Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions UAI 2026 Structure Learning for Unfaithful Distributions: The Minimal Dependence Faithfulness UAI 2026 $β$-th order Acyclicity Derivatives for DAG Learning AISTATS 2025 Analytic DAG Constraints for Differentiable DAG Learning ICLR 2025 Causal Discovery on Dependent Binary Data AISTATS 2025 Continuous Structure Constraint Integration for Robust Causal Discovery AISTATS 2025 Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders AISTATS 2025 Expert-In-The-Loop Causal Discovery: Iterative Model Refinement Using Expert Knowledge UAI 2025 Extendable and Iterative Structure Learning Strategy for Bayesian Networks ICLR 2025 Integer Programming Based Methods and Heuristics for Causal Graph Learning AISTATS 2025 Nonlinear Causal Discovery for Grouped Data UAI 2025 Adjustment Identification Distance: A gadjid for Causal Structure Learning UAI 2024 Latent Gaussian Graphical Models with Golazo Penalty PGM 2024 Learning Staged Trees from Incomplete Data PGM 2024 Localised Natural Causal Learning Algorithms for Weak Consistency Conditions UAI 2024 Markov Equivalence and Consistency in Differentiable Structure Learning NeurIPS 2024 Learning to Induce Causal Structure ICLR 2023 Highly Efficient Structural Learning of Sparse Staged Trees PGM 2022 Differentiable TAN Structure Learning for Bayesian Network Classifiers PGM 2020 Discriminative Non-Parametric Learning of Arithmetic Circuits PGM 2020