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Quasi-Monte Carlo: structure in the randomness for better sampling

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Quasi-Monte Carlo (QMC) sampling is well-established as a universal tool to improve the convergence of MC methods, improving the concentration properties of estimators by using low-discrepancy samples to reduce integration error. They replace i.i.d. samples with a correlated ensemble, carefully constructed to be more ‘diverse’ and hence improve approximation quality. In this reading group we will discuss both traditional QMC schemes for approximating integrals R^d and recently-proposed counterparts for discrete space, teasing out their common themes as we progress towards a general recipe for more efficient sampling.

This talk is part of the Machine Learning Reading Group @ CUED series.

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