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20232026
most citedWhom to Trust? Elective Learning for Distributed Gaussian Process Regression

3 citations · 7 across the 19 of their papers we have counts for

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14 papers · 1 filter

eess.SY2026

Where to Put Safety? Control Barrier Function Placement in Networked Control Systems

Severin Beger, Yuling Chen, Sandra Hirche

Control barrier functions (CBFs) are widely used to enforce safety in autonomous systems, yet their placement within networked control architectures remains largely unexplored. In…

eess.SY2026

Online Bayesian Learning of Agent Behavior in Differential Games

Francesco Bianchin, Robert Lefringhausen, Sandra Hirche

This work introduces an online Bayesian game-theoretic method for behavior identification in multi-agent dynamical systems. By casting Hamilton-Jacobi-Bellman optimality conditions…

eess.SY2025

Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control

Robert Lefringhausen, Theodor Springer, Sandra Hirche

Reliable optimal control is challenging when the dynamics of a nonlinear system are unknown and only infrequent, noisy output measurements are available. This work addresses this s…

eess.SY2025

A Set-Theoretic Robust Control Approach for Linear Quadratic Games with Unknown Counterparts

Francesco Bianchin, Robert Lefringhausen, Elisa Gaetan +2

Ensuring robust decision-making in multi-agent systems is challenging when agents have distinct, possibly conflicting objectives and lack full knowledge of each other's strategies.…

eess.SY2025

Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference

Robert Lefringhausen, Sami Leon Noel Aziz Hanna, Elias August +1

Certifying safety in dynamical systems is crucial, but barrier certificates - widely used to verify that system trajectories remain within a safe region - typically require explici…

eess.SY2024

Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems

Sihua Zhang, Di-Hua Zhai, Xiaobing Dai +3

With the increasing complexity of real-world systems and varying environmental uncertainties, it is difficult to build an accurate dynamic model, which poses challenges especially…