Dr. Jacob Kauffmann
Dr. Jacob Kauffmann
Postdoctoral Researcher
Research Interests
- Unsupervised Learning
- Explainable AI
- Clustering
- Anomaly Detection
- Kernel Methods
- Neural Networks
Philip Naumann, Jacob Kauffmann, Klaus-Robert Müller, Grégoire Montavon
Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport
A. Singha, J. Kauffmann, E. Cellini, K. Jansen, S. Nakajima
Scalable Generative Sampling and Multilevel Estimation for Lattice Field Theories Near Criticality
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
Philip Naumann, Jacob Kauffmann, Grégoire Montavon
Wasserstein Distances Made Explainable: Insights into Dataset Shifts and Transport Phenomena
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.