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stat.ML2026
Kernel Density Machines
Andrea Della Vecchia, Damir Filipovic, Paul Schneider
We introduce kernel density machines (KDM), an agnostic kernel-based framework for learning the Radon-Nikodym derivative (density) between probability measures under minimal assump…
stat.ML2026
Transfer Learning Across Fixed-Income Product Classes
Nicolas Camenzind, Damir Filipovic
We propose a framework for transfer learning of discount curves across different fixed-income product classes. Motivated by challenges in estimating discount curves from sparse or…
stat.ML2024
Adaptive joint distribution learning
Damir Filipovic, Michael Multerer, Paul Schneider
We develop a new framework for estimating joint probability distributions using tensor product reproducing kernel Hilbert spaces (RKHS). Our framework accommodates a low-dimensiona…