# Deep learning for ranking response surfaces with applications to optimal stopping problems

@article{Hu2019DeepLF, title={Deep learning for ranking response surfaces with applications to optimal stopping problems}, author={Ruimeng Hu}, journal={Quantitative Finance}, year={2019}, volume={20}, pages={1567 - 1581} }

In this paper, we propose deep learning algorithms for ranking response surfaces with applications to optimal stopping problems in financial mathematics. The problem of ranking response surfaces is motivated by estimating optimal feedback policy maps in stochastic control problems, aiming to efficiently find the index associated with the minimal response across the entire continuous input space . By considering points in as pixels and indices of the minimal surfaces as labels, we recast the… Expand

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