Large-scale local surrogate modeling of stochastic simulation experiments

DA Cole, RB Gramacy, M Ludkovski - Computational Statistics & Data …, 2022 - Elsevier
Gaussian process (GP) surrogate modeling for large computer experiments is limited by
cubic runtimes, especially with data from stochastic simulations with input-dependent noise …

mlOSP: Towards a unified implementation of regression Monte Carlo algorithms

M Ludkovski - arXiv preprint arXiv:2012.00729, 2020 - arxiv.org
We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping
Problems. The template is implemented in the R statistical environment and publicly …

Sequential metamodel‐based approaches to level‐set estimation under heteroscedasticity

Y Zhang, X Chen - Statistical Analysis and Data Mining: The …, 2024 - Wiley Online Library
This paper proposes two sequential metamodel‐based methods for level‐set estimation
(LSE) that leverage the uniform bound built on stochastic kriging: predictive variance …

Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method

B Barracosa, J Bect, HD Baraffe, J Morin… - arXiv preprint arXiv …, 2022 - arxiv.org
This article focuses on the multi-objective optimization of stochastic simulators with high
output variance, where the input space is finite and the objective functions are expensive to …

Toward a unified implementation of regression Monte Carlo algorithms

M Ludkovski - Journal of Computational Finance, 2023 - papers.ssrn.com
We introduce mlOSP, a computational template for machine learning for optimal stopping
problems, which is implemented in the R statistical environment and publicly available via a …

Consistency and Uniform Bounds for Heteroscedastic Simulation Metamodeling and Their Applications

Y Zhang - 2023 - vtechworks.lib.vt.edu
Heteroscedastic metamodeling has gained popularity as an effective tool for analyzing and
optimizing complex stochastic systems. A heteroscedastic metamodel provides an accurate …

Complex Theory and Batch Processing in Mechanical Systemic Data Extraction

X Chang, H Pan, J Xu, S Qiao, T Wang - IEEE Access, 2022 - ieeexplore.ieee.org
This paper designs a new batching program to extract the original data, which helps to
traverse the entire sample space quickly and provides a new approach for data extraction …