activity
20222024
most citedLearning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search

8 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2024

Analyzing Closed-loop Training Techniques for Realistic Traffic Agent Models in Autonomous Highway Driving Simulations

Matthias Bitzer, Reinis Cimurs, Benjamin Coors +4

Simulation plays a crucial role in the rapid development and safe deployment of autonomous vehicles. Realistic traffic agent models are indispensable for bridging the gap between s…

cs.LG2023

Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels

Matthias Bitzer, Mona Meister, Christoph Zimmer

Learning the kernel parameters for Gaussian processes is often the computational bottleneck in applications such as online learning, Bayesian optimization, or active learning. Amor…

cs.LG2023

Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems

Matthias Bitzer, Mona Meister, Christoph Zimmer

Learning precise surrogate models of complex computer simulations and physical machines often require long-lasting or expensive experiments. Furthermore, the modeled physical depen…

cs.LG2022★ 2 cited

Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport

Matthias Bitzer, Mona Meister, Christoph Zimmer

Despite recent advances in automated machine learning, model selection is still a complex and computationally intensive process. For Gaussian processes (GPs), selecting the kernel…

cs.LG2022★ 8 cited

Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search

Karl Kurzer, Matthias Bitzer, J. Marius Zöllner

Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios that require a high degree of cooperation between traffic participants. However, for coop…