1 citations · 1 across the 5 of their papers we have counts for
5 papers
Approximation to Deep Q-Network by Stochastic Delay Differential Equations
Jianya Lu, Yingjun Mo
Despite the significant breakthroughs that the Deep Q-Network (DQN) has brought to reinforcement learning, its theoretical analysis remains limited. In this paper, we construct a s…
Distribution estimation and change-point estimation for time series via DNN-based GANs
Jianya Lu, Yingjun Mo, Zhijie Xiao +2
The generative adversarial networks (GANs) have recently been applied to estimating the distribution of independent and identically distributed data, and have attracted a lot of re…
Almost sure invariance principle of mixing time series in Hilbert space
Jianya Lu, Wei Biao Wu, Zhijie Xiao +1
Inspired by \citet{Berkes14} and \citet{Wu07}, we prove an almost sure invariance principle for stationary mixing stochastic processes defined on Hilbert space. Our result can…
Approximation to stochastic variance reduced gradient Langevin dynamics by stochastic delay differential equations
Peng Chen, Jianya Lu, Lihu Xu
We study in this paper a weak approximation to stochastic variance reduced gradient Langevin dynamics by stochastic delay differential equations in Wasserstein-1 distance, and obta…
Central limit theorem and Self-normalized Cramér-type moderate deviation for Euler-Maruyama Scheme
Jianya Lu, Yuzhen Tan, Lihu Xu
We consider a stochastic differential equation and its Euler-Maruyama (EM) scheme, under some appropriate conditions, they both admit a unique invariant measure, denoted by and…