Dr. Jacob Kauffmann
Dr. Jacob Kauffmann
Postdoctoral Researcher
Research Interests
- Unsupervised Learning
- Explainable AI
- Clustering
- Anomaly Detection
- Kernel Methods
- Neural Networks
Florian Bley, Jacob Kauffmann, Simon León Krug, Klaus-Robert Müller, Grégoire Montavon
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
August
05,
2025
https://doi.org/10.48550/arXiv.2508.03913
Philip Naumann, Jacob Kauffmann, Grégoire Montavon
Wasserstein Distances Made Explainable: Insights into Dataset Shifts and Transport Phenomena
May
09,
2025
https://doi.org/10.48550/arXiv.2505.06123
Explainable AI| Mar 03, 2026
Wasserstein distances made explainable
BIFOLD scientists developed a novel framework to make a widely used foundational statistical tool, the Wasserstein distance, interpretable in machine learning and data analysis contexts.