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stat.ML2025
The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
We present a novel kernel-based method for learning multivariate stochastic differential equations (SDEs). The method follows a two-step procedure: we first estimate the drift term…
stat.ML2025
ROCK: A variational formulation for occupation kernel methods in Reproducing Kernel Hilbert Spaces
Victor Rielly, Kamel Lahouel, Chau Nguyen +3
We present a Representer Theorem result for a large class of weak formulation problems. We provide examples of applications of our formulation both in traditional machine learning…