Learning Joint Graphical Model with Computational Efficiency, Dynamic Regularization, and Adaptation
联合图模型的学习:计算效率、动态正则化与自适应性
Multi-sourced datasets are common in studies of variable interactions, for example, individual-level fMRI integration, cross-domain recommendation, etc, where each source induces a related but distinct dependency structure. Joint learning of multiple graphical models (i.e., multiple precision matrices) has emerged as an important tool in analyzing such data. Unlike separate learning, joint learning can leverage shared structural patterns across graphs to yield more accurate…