17 citations · 81 across the 20 of their papers we have counts for
5 papers · 1 filter
Gray Box Identification of State-Space Models Using Difference of Convex Programming
Chengpu Yu, Lennart Ljung, Michel Verhaegen
Gray-box identification is prevalent in modeling physical and networked systems. However, due to the non-convex nature of the gray-box identification problem, good initial paramete…
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden +1
Bayesian optimization through Gaussian process regression is an effective method of optimizing an unknown function for which every measurement is expensive. It approximates the obj…
Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions
Laurens Bliek, Hans R. G. W. Verstraete, Michel Verhaegen +1
This paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements. The algorithm maintains a surrogat…
Mean and variance of the LQG cost function
Hildo Bijl, Jan Willem van Wingerden, Thomas B. Schön +1
Linear Quadratic Gaussian (LQG) systems are well-understood and methods to minimize the expected cost are readily available. Less is known about the statistical properties of the r…
System Identification through Online Sparse Gaussian Process Regression with Input Noise
Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden +1
There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have…