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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

机译：一种用于高维积分的机器学习求解器：通过具有Malliavin权重的SDES的高阶弱近似方案来解决Kolmogorov PDE和随机梯度下降

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Riu Naito; Toshihiro Yamada;
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年度 2020
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1. A higher order weak approximation scheme of multidimensional stochastic differential equations using Malliavin weights [J] . Yamada Toshihiro Journal of Computational and Applied Mathematics . 2017,第期

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2. Method of Successive Weighted Averages (MSWA) and Self-Regulated Averaging Schemes for Solving Stochastic User Equilibrium Problem [J] . Henry X. Liu, Xiaozheng He, Bingsheng He Networks and spatial economics . 2009,第4期

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3. Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning: Examining Distributed and Centralized Stochastic Gradient Descent [J] . Pu Shi, Olshevsky Alex, Paschalidis Ioannis Ch. IEEE Signal Processing Magazine . 2020,第3期

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5. Stochastic Gradient Descent for Modern Machine Learning: Theory, Algorithms and Applications [D] . Kidambi, Rahul. 2019

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6. Applications of Spectral Gradient Algorithm for Solving Matrix ℓ21-Norm Minimization Problems in Machine Learning [O] . Yunhai Xiao, Qiuyu Wang, Lihong Liu -1

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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

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