3 papers in the archive (2025–2026), average empirical rigor 4.3/10, average math complexity 9/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Machine Learning for Market Forecasting, Options & Derivatives Pricing.
Frequent co-authors: Yuming Ma (2), Yuji Shinozaki (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%).
High-order recombination keeps weak approximation trees of diffusions small by a moment-preserving reduction of the measure on each patch of nodes. The weighted Litterer–Lyons (WLL) test of Ninomiya and Shinozaki sets the patch scale using a scalar \(λ>0\) chosen beforehand, whose appropriate value
A new deep-learning neural network architecture based on high-order weak approximation algorithms for stochastic differential equations (SDEs) is proposed. The architecture enables the efficient learning of martingales by deep learning models. The behaviour of deep neural networks based on this arch
This paper presents an algorithm for applying the high-order recombination method, originally introduced by Lyons and Litterer in ``High-order recombination and an application to cubature on Wiener space’’ (Ann. Appl. Probab. 22(4):1301–1327, 2012), to practical problems in mathematical finance. A r