4 papers
Learning Graph Topology with Functional Priors via Bilevel Optimization
Chenyue Zhang, Shangyuan Liu, Hoi-To Wai +1
Learning graph topology of complex networks is challenging due to limited data availability and imprecise data models. Different from prior works that focus on structural priors wi…
Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
Linglingzhi Zhu, Wentao Ding, Shangyuan Liu +1
Recent advancements in data science have significantly elevated the importance of orthogonally constrained optimization problems. The Riemannian approach has become a popular techn…
Network Games Induced Prior for Graph Topology Learning
Chenyue Zhang, Shangyuan Liu, Hoi-To Wai +1
Learning the graph topology of a complex network is challenging due to limited data availability and imprecise data models. A common remedy in existing works is to incorporate prio…
LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
Shangyuan Liu, Linglingzhi Zhu, Anthony Man-Cho So
Graph learning from signals is a core task in Graph Signal Processing (GSP). One of the most commonly used models to learn graphs from stationary signals is SpecT. However, its pra…