26 citations · 31 across the 4 of their papers we have counts for
5 papers
Preferential Bayesian Optimization with Crash Feedback
Johanna Menn, David Stenger, Sebastian Trimpe
Bayesian optimization is a popular black-box optimization method for parameter learning in control and robotics. It typically requires an objective function that reflects the user'…
A Systems-Theoretic View on the Convergence of Algorithms under Disturbances
Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach
Algorithms increasingly operate within complex physical, social, and engineering systems where they are exposed to disturbances, noise, and interconnections with other dynamical sy…
Distributed Event-Based Learning via ADMM
Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach
We consider a distributed learning problem, where agents minimize a global objective function by exchanging information over a network. Our approach has two distinct features: (i)…
Event-based State Estimation: An Emulation-based Approach
Sebastian Trimpe
An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor agents observe a dynamic process and spor…
Distributed Event-based State Estimation
Sebastian Trimpe
An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor-actuator-agents observe a dynamic process…