3 papers
math.NA2026
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…
math.NA2025
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…
cs.LG2025
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…