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cs.LG2026
Distributional Loss for Robust Classification
Kathleen Anderson, Thomas Martinetz
This paper proposes a novel loss concept for supervised classification tasks. Rather than enforcing a direct mapping from each input sample to a single assigned label, we define an…
cs.LG2026
Nearest-Neighbor Density Estimation for Dependency Suppression
Kathleen Anderson, Thomas Martinetz
The ability to remove unwanted dependencies from data is crucial in various domains, including fairness, robust learning, and privacy protection. In this work, we propose an encode…