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Riu Naito

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2020 – today

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2024
[j3]
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  - journals/entropy/KannariNY24
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  - https://dblp.org/rec/journals/entropy/KannariNY24
Masaya Kannari, Riu Naito, Toshihiro Yamada:
Asymptotic Expansion and Weak Approximation for a Stochastic Control Problem on Path Space. Entropy 26(2): 119 (2024)
[i2]
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  - journals/corr/abs-2410-09666
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Reiichiro Kawai, Riu Naito, Toshihiro Yamada:
A forward scheme with machine learning for forward-backward SDEs with jumps by decoupling jumps. CoRR abs/2410.09666 (2024)
2022
[c1]
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  - conf/cifer/NaitoY22
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Riu Naito, Toshihiro Yamada:
A deep learning-based high-order operator splitting method for high-dimensional nonlinear parabolic PDEs via Malliavin calculus: application to CVA computation. CIFEr 2022: 1-8
2020
[i1]
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  - journals/corr/abs-2012-12346
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Riu Naito, Toshihiro Yamada:
A machine learning solver for high-dimensional integrals: Solving Kolmogorov PDEs by stochastic weighted minimization and stochastic gradient descent through a high-order weak approximation scheme of SDEs with Malliavin weights. CoRR abs/2012.12346 (2020)

2010 – 2019

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2019
[j2]
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  - journals/mcma/NaitoY19
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Riu Naito, Toshihiro Yamada:
A third-order weak approximation of multidimensional Itô stochastic differential equations. Monte Carlo Methods Appl. 25(2): 97-120 (2019)
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  - journals/mcma/NaitoY19a
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Riu Naito, Toshihiro Yamada:
A second-order discretization for forward-backward SDEs using local approximations with Malliavin calculus. Monte Carlo Methods Appl. 25(4): 341-361 (2019)

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