1 citations · 1 across the 6 of their papers we have counts for
6 papers
Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm
Siming Liang, Hui Sun, Richard Archibald +1
This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The al…
An Online Algorithm for Solving Feedback Optimal Control Problems with Partial Observations
Siming Liang, Ruoyu Hu, Feng Bao +2
This paper presents a novel methodology to tackle feedback optimal control problems in scenarios where the exact state of the controlled process is unknown. It integrates data assi…
A PDE-based Adaptive Kernel Method for Solving Optimal Filtering Problems
Zezhong Zhang, Richard Archibald, Feng Bao
In this paper, we introduce an adaptive kernel method for solving the optimal filtering problem. The computational framework that we adopt is the Bayesian filter, in which we recur…
A Kernel Learning Method for Backward SDE Filter
Richard Archibald, Feng Bao
In this paper, we develop a kernel learning backward SDE filter method to estimate the state of a stochastic dynamical system based on its partial noisy observations. A system of f…
A Backward SDE Method for Uncertainty Quantification in Deep Learning
Richard Archibald, Feng Bao, Yanzhao Cao +1
We develop a probabilistic machine learning method, which formulates a class of stochastic neural networks by a stochastic optimal control problem. An efficient stochastic gradient…
An efficient numerical algorithm for solving data driven feedback control problems
Richard Archibald, Feng Bao, Jiongmin Yong +1
The goal of this paper is to solve a class of stochastic optimal control problems numerically, in which the state process is governed by an Itô type stochastic differential equatio…