10 citations · 13 across the 9 of their papers we have counts for
12 papers · 1 filter
Admissable: Training Reinforcement Learning Agents against Adversarial Missingness
Paul Stahlhofen, Luca Hermes, Tim Kochs +2
In order to make Reinforcement Learning algorithms applicable in real world scenarios, safety must be ensured even under adverse operating conditions. In this work, we consider the…
MeGA-MP: Metric Graph Advection Message Passing -- A Physics-Informed Message Passing Operator for Advection-Dominated Metric Graphs
Janine Strotherm, Luca Hermes, André Artelt +1
Many real-world systems are organized as networks where spatio-temporal dynamics unfold along connections and not discretely between nodes. Examples include utility networks such a…
DGPO: RL-Steered Graph Diffusion for Neural Architecture Generation
Aleksei Liuliakov, Luca Hermes, Barbara Hammer
Reinforcement learning fine-tuning has proven effective for steering generative diffusion models toward desired properties in image and molecular domains. Graph diffusion models ha…
Noise Robust One-Class Intrusion Detection on Dynamic Graphs
Aleksei Liuliakov, Alexander Schulz, Luca Hermes +1
In the domain of network intrusion detection, robustness against contaminated and noisy data inputs remains a critical challenge. This study introduces a probabilistic version of t…
One-Class Intrusion Detection with Dynamic Graphs
Aleksei Liuliakov, Alexander Schulz, Luca Hermes +1
With the growing digitalization all over the globe, the relevance of network security becomes increasingly important. Machine learning-based intrusion detection constitutes a promi…
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks
Maximilian Muschalik, Fabian Fumagalli, Paolo Frazzetto +5
Albeit the ubiquitous use of Graph Neural Networks (GNNs) in machine learning (ML) prediction tasks involving graph-structured data, their interpretability remains challenging. In…