3 papers
math.OC2026
Koopman Lifting with Certified Error Bounds for Joint Inference in Nonlinear Networks
Chuansen Peng, Xiaojing Shen, Yunmin Zhu
Jointly inferring latent node states and unknown network topology in nonlinear graphical dynamical systems is a fundamental yet largely unsolved problem, where the mutual entanglem…
cs.LG2026
Dynamic Elliptical Graph Factor Models via Riemannian Optimization with Geodesic Temporal Regularization
Chuansen Peng, Xiaojing Shen
Inferring time-varying graph structures from high-dimensional nodal observations is a fundamental problem arising in neuroscience, finance, climatology, and beyond. Two intrinsic c…
stat.ML2026
Learning Time-Varying Graphs from Incomplete Graph Signals
Chuansen Peng, Xiaojing Shen
This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially observed graph signals. We propose a unifie…