4 citations · 4 across the 7 of their papers we have counts for
8 papers
Convergence and Regret of the Policy Gradient for Multi-Armed Bandits in Diffusion Environment
Yanwei Jia, Du Ouyang
This paper studies the policy gradient update for a multi-arm bandit problem in diffusion environment that is described by a stochastic differential equation (SDE) under the contin…
A Zeroth-Order Deep Learning Method for Fully Nonlinear Parabolic Partial Differential Equations with Unknown Coefficients
Yanwei Jia, Du Ouyang, Huyên Pham +1
High-dimensional partial differential equations (PDEs) with unknown coefficients arise widely in scientific machine learning, including continuous-time reinforcement learning, yet…
Quasi-Monte Carlo for SDE Simulation: Error Analysis and Dimensionality Reduction
Du Ouyang, Zexin Pan, Zhijian He
We investigate the numerical simulation of general stochastic differential equations (SDEs) using Quasi-Monte Carlo (QMC) methods. First, we provide a rigorous theoretical analysis…
Uncertainty quantification using importance-sampled quasi-Monte Carlo with dimension-independent convergence rates
Zexin Pan, Du Ouyang, Zhijian He
Quasi-Monte Carlo (QMC) integration over unbounded domains remains challenging due to the high dimensionality of sampling space and the boundary growth of the integr…
Quasi-Monte Carlo integration over with boundary-damping importance sampling
Zexin Pan, Du Ouyang, Zhijian He
This paper proposes a new importance sampling (IS) that is tailored to quasi-Monte Carlo (QMC) integration over . IS introduces a multiplicative adjustment to the int…
Accuracy of Discretely Sampled Stochastic Policies in Continuous-time Reinforcement Learning
Yanwei Jia, Du Ouyang, Yufei Zhang
Stochastic policies (also known as relaxed controls) are widely used in continuous-time reinforcement learning algorithms. However, executing a stochastic policy and evaluating its…
Generalization Error Analysis of Deep Backward Dynamic Programming for Solving Nonlinear PDEs
Du Ouyang, Jichang Xiao, Xiaoqun Wang
We explore the application of the quasi-Monte Carlo (QMC) method in deep backward dynamic programming (DBDP) (Hure et al. 2020) for numerically solving high-dimensional nonlinear p…
Quasi-Monte Carlo for unbounded integrands with importance sampling
Du Ouyang, Xiaoqun Wang, Zhijian He
We consider the problem of estimating an expectation by quasi-Monte Carlo (QMC) methods, where is an unbounded smooth function on $ \mathbb{R}…