Introduction to Quasi-Monte Carlo Integration and Applications

By Gunther Leobacher, Friedrich Pillichshammer. Compact Textbooks in Mathematics, 2014

138 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1f67e62968cb22d6a7dcab57da01aca684e871b5

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1. Mihaela Rosca
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Citations in the corpus

• Monte Carlo Gradient Estimation in Machine Learning (2019, methodology): “As an example, quasi-Monte Carlo methods generate a set of samples that form a minimum discrepancy sequence, which are both more efficient in the use of the samples and can lead to reduced variance (Glasserman (2013, chp. 5), Leobacher and Pillichshammer (2014), Buchholz et al. (2018)).”