# Convergence guarantees for kernel-based quadrature rules in misspecified settings

@inproceedings{Kanagawa2016ConvergenceGF, title={Convergence guarantees for kernel-based quadrature rules in misspecified settings}, author={Motonobu Kanagawa and Bharath K. Sriperumbudur and Kenji Fukumizu}, booktitle={NIPS}, year={2016} }

Kernel-based quadrature rules are becoming important in machine learning and statistics, as they achieve super-$\sqrt{n}$ convergence rates in numerical integration, and thus provide alternatives to Monte Carlo integration in challenging settings where integrands are expensive to evaluate or where integrands are high dimensional. These rules are based on the assumption that the integrand has a certain degree of smoothness, which is expressed as that the integrand belongs to a certain…

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