  We study quadrature rules for functions from an RKHS, using nodes sampled
from a determinantal point process (DPP). DPPs are parametrized by a kernel,
and we use a truncated and saturated version of the RKHS kernel. This link
between the two kernels, along with DPP machinery, leads to relatively tight
bounds on the quadrature error, that depends on the spectrum of the RKHS
kernel. Finally, we experimentally compare DPPs to existing kernel-based
quadratures such as herding, Bayesian quadrature, or leverage score sampling.
Numerical results confirm the interest of DPPs, and even suggest faster rates
than our bounds in particular cases.
