6 papers
Local Preferential Bayesian Optimization
Johanna Menn, Miriam Kober, Paul Brunzema +2
Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function. Preferential…
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'…
Local Entropy Search over Descent Sequences for Bayesian Optimization
David Stenger, Armin Lindicke, Alexander von Rohr +1
Searching large and complex design spaces for a global optimum can be infeasible and unnecessary. A practical alternative is to iteratively refine the neighborhood of an initial de…
Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF
Tobin Holtmann, David Stenger, Andres Posada-Moreno +2
State estimation in control and systems engineering traditionally requires extensive manual system identification or data-collection effort. However, transformer-based foundation m…
Early Stopping Bayesian Optimization for Controller Tuning
David Stenger, Dominik Scheurenberg, Heike Vallery +1
Manual tuning of performance-critical controller parameters can be tedious and sub-optimal. Bayesian Optimization (BO) is an increasingly popular practical alternative to automatic…
Local Bayesian Optimization for Controller Tuning with Crash Constraints
Alexander von Rohr, David Stenger, Dominik Scheurenberg +1
Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method…