gaussian-graphical-models

This paper presents a polynomial-time algorithm for learning the structure of a Gaussian graphical model from a single trajectory of Glauber dynamics, with a trajectory-length guarantee that does not depend on the mixing time.

This paper introduces PACE-GGM, a differentially private method for covariance estimation that adaptively selects and measures the most informative entries of the empirical covariance matrix, using Gaussian graphical models for reconstruction. It shows improved estimation error over baselines on real-world data, especially in high-dimensional settings.