9 papers
Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch
Paul Brunzema, Louis Tiao, Nhat Le +3
Bayesian optimization (BO) has become the standard tool for sample-efficient optimization and owes its efficiency to uncertainty-aware search driven by generic statistical priors.…
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…
BayeSQP: Bayesian Optimization through Sequential Quadratic Programming
Paul Brunzema, Sebastian Trimpe
We introduce BayeSQP, a novel algorithm for general black-box optimization that merges the structure of sequential quadratic programming with concepts from Bayesian optimization. B…
The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning
Henrik Hose, Paul Brunzema, Devdutt Subhasish +1
The development of robust learning-based control algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a signif…
Vision-Conditioned Variational Bayesian Last Layer Dynamics Models
Paul Brunzema, Thomas Lew, Ray Zhang +3
Agile control of robotic systems often requires anticipating how the environment affects system behavior. For example, a driver must perceive the road ahead to anticipate available…
Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
Henrik Hose, Paul Brunzema, Alexander von Rohr +3
Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network, removing the need to solve an expensive optimization problem at runtime. Howeve…