Graphical Model

Graphical models represent complex relationships between variables using graphs, aiming to infer conditional dependencies and causal structures from data. Current research emphasizes developing efficient algorithms for inference and structure learning in high-dimensional settings, particularly focusing on methods like graphical Lasso, alternating direction method of multipliers (ADMM), and variations of greedy equivalence search, often incorporating fairness considerations and handling incomplete data via techniques such as optimal transport. These advancements improve the accuracy, interpretability, and scalability of graphical models, impacting diverse fields including machine learning, causal inference, and financial modeling.