506 citations · 520 across the 7 of their papers we have counts for
Showing 2022Show all
2 papers · 1 filter
cs.LG2022★ 1 cited
Quantifying probabilistic robustness of tree-based classifiers against natural distortions
Christoph Schweimer, Sebastian Scher
The concept of trustworthy AI has gained widespread attention lately. One of the aspects relevant to trustworthy AI is robustness of ML models. In this study, we show how to probab…
cs.LG2022★ 2 cited
Testing robustness of predictions of trained classifiers against naturally occurring perturbations
Sebastian Scher, Andreas Trügler
Correctly quantifying the robustness of machine learning models is a central aspect in judging their suitability for specific tasks, and ultimately, for generating trust in them. W…