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cs.LG2026
Explainably Safe Reinforcement Learning
Sabine Rieder, Stefan Pranger, Debraj Chakraborty +2
Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior. This is particularly important for learned systems, whos…
cs.LG2024
Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces
Vahid Hashemi, Jan KÅetÃnský, Sabine Rieder +2
Since neural networks can make wrong predictions even with high confidence, monitoring their behavior at runtime is important, especially in safety-critical domains like autonomous…
cs.LG2024
Monitizer: Automating Design and Evaluation of Neural Network Monitors
Muqsit Azeem, Marta Grobelna, Sudeep Kanav +3
The behavior of neural networks (NNs) on previously unseen types of data (out-of-distribution or OOD) is typically unpredictable. This can be dangerous if the network's output is u…