activity
20122024
most citedWasserstein Adversarial Imitation Learning

35 citations · 50 across the 10 of their papers we have counts for

collaborators

11 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

Learning Hybrid Dynamics Models With Simulator-Informed Latent States

Katharina Ensinger, Sebastian Ziesche, Sebastian Trimpe

Dynamics model learning deals with the task of inferring unknown dynamics from measurement data and predicting the future behavior of the system. A typical approach to address this…

cs.LG2023

Exact Inference for Continuous-Time Gaussian Process Dynamics

Katharina Ensinger, Nicholas Tagliapietra, Sebastian Ziesche +1

Physical systems can often be described via a continuous-time dynamical system. In practice, the true system is often unknown and has to be learned from measurement data. Since dat…

cs.LG2023★ 1 cited

Combining Slow and Fast: Complementary Filtering for Dynamics Learning

Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch +2

Modeling an unknown dynamical system is crucial in order to predict the future behavior of the system. A standard approach is training recurrent models on measurement data. While t…

cs.LG2021★ 1 cited

Structure-preserving Gaussian Process Dynamics

Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche +2

Most physical processes posses structural properties such as constant energies, volumes, and other invariants over time. When learning models of such dynamical systems, it is criti…

cs.LG2019★ 35 cited

Wasserstein Adversarial Imitation Learning

Huang Xiao, Michael Herman, Joerg Wagner +3

Imitation Learning describes the problem of recovering an expert policy from demonstrations. While inverse reinforcement learning approaches are known to be very sample-efficient i…

math.PR2016★ 2 cited

First passage percolation in Euclidean space and on random tessellations

Sebastian Ziesche

There are various models of first passage percolation (FPP) in . We want to start a very general study of this topic. To this end we generalize the first passage perco…

math.PR2016★ 3 cited

Bernoulli Percolation on random Tessellations

Sebastian Ziesche

We generalize the standard site percolation model on the -dimensional lattice to a model on random tessellations of . We prove the uniqueness of the infinite cluste…

math.PR2016

Sharpness of the phase transition and lower bounds for the critical intensity in continuum percolation on

Sebastian Ziesche

We consider the Boolean model on with random compact grains, i.e. where is a Poisson p…

math.PR2016

On the Ornstein-Zernike equation for stationary cluster processes and the random connection model

Günter Last, Sebastian Ziesche

In the first part of this paper we consider a general stationary subcritical cluster model in . The associated pair-connectedness function can be defined in terms of…

cs.IT2012★ 8 cited

Efficient Majority-Logic Decoding of Short-Length Reed--Muller Codes at Information Positions

Peter Hauck, Michael Huber, Juliane Bertram +2

Short-length Reed--Muller codes under majority-logic decoding are of particular importance for efficient hardware implementations in real-time and embedded systems. This paper sign…