14 citations · 44 across the 10 of their papers we have counts for
3 papers · 1 filter
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
Andreas Sedlmeier, Thomas Gabor, Thomy Phan +2
Robustness to out-of-distribution (OOD) data is an important goal in building reliable machine learning systems. Especially in autonomous systems, wrong predictions for OOD inputs…
Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm
Thomas Gabor, Marie Kiermeier, Andreas Sedlmeier +5
From formal and practical analysis, we identify new challenges that self-adaptive systems pose to the process of quality assurance. When tackling these, the effort spent on various…
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
Andreas Sedlmeier, Thomas Gabor, Thomy Phan +2
We consider the problem of detecting out-of-distribution (OOD) samples in deep reinforcement learning. In a value based reinforcement learning setting, we propose to use uncertaint…