3 papers in the archive (2023–2025), average empirical rigor 3.9/10, average math complexity 9.2/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Machine Learning for Market Forecasting, Options & Derivatives Pricing, Stochastic Control & Optimal Stopping.
Frequent co-authors: Ariel Neufeld (2), Christa Cuchiero (1), Josef Teichmann (1), Sizhou Wu (1), Anastasis Kratsios (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
In this paper, we present a randomized extension of the deep splitting algorithm introduced in [Beck, Becker, Cheridito, Jentzen, and Neufeld (2021)] using random neural networks suitable to approximately solve both high-dimensional nonlinear parabolic PDEs and PIDEs with jumps having (possibly) inf
Neural operators (NOs) are a class of deep learning models designed to simultaneously solve infinitely many related problems by casting them into an infinite-dimensional space, whereon these NOs operate. A significant gap remains between theory and practice: worst-case parameter bounds from universa
We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space. To this end, we use an additive family to map the input weighted space to the hidden layer, on which a non-linear scalar