Author

Edouard Motte

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#machine learning Preprint Sep 2026

Deep kernel hedging

A deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods and derives a generalized representer theorem for the joint hedging problem, reducing the empirical optimization to a finite-dimensional problem is introduced.

Jean-Loup Dupret, Donatien Hainaut, Edouard Motte · 0 citations