114 citations · 314 across the 8 of their papers we have counts for
8 papers · 1 filter
A Full Bayesian Approach to Sparse Network Inference using Heterogeneous Datasets
Junyang Jin, Ye Yuan, Jorge Goncalves
Network inference has been attracting increasing attention in several fields, notably systems biology, control engineering and biomedicine. To develop a therapy, it is essential to…
High Precision Variational Bayesian Inference of Sparse Linear Networks
Junyang Jin, Ye Yuan, Jorge Goncalves
Sparse networks can be found in a wide range of applications, such as biological and communication networks. Inference of such networks from data has been receiving considerable at…
Blind Identification of Fully Observed Linear Time-Varying Systems via Sparse Recovery
Roel Dobbe, Stephan Liu, Ye Yuan +1
Discrete-time linear time-varying (LTV) systems form a powerful class of models to approximate complex dynamical systems with nonlinear dynamics for the purpose of analysis, design…
Event Detection and Localization in Distribution Grids with Phasor Measurement Units
Omid Ardakanian, Ye Yuan, Roel Dobbe +3
The recent introduction of synchrophasor technology into power distribution systems has given impetus to various monitoring, diagnostic, and control applications, such as system id…
Inverse Power Flow Problem
Ye Yuan, Steven Low, Omid Ardakanian +1
This paper formulates an inverse power flow problem which is to infer a nodal admittance matrix (hence the network structure of a power system) from voltage and current phasors mea…
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach
J. Jin, Y. Yuan, W. Pan +4
This paper begins with considering the identification of sparse linear time-invariant networks described by multivariable ARX models. Such models possess relatively simple structur…
Sparse Bayesian Inference of Multivariable ARX Networks
J. Jin, Y. Yuan, A. Webb +1
Increasing attention has recently been given to the inference of sparse networks. In biology, for example, most molecules only bind to a small number of other molecules, leading to…
On the Powerball Method for Optimization
Ye Yuan, Mu Li, Jun Liu +1
We propose a new method to accelerate the convergence of optimization algorithms. This method simply adds a power coefficient to the gradient during optimization. We ca…