Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding

Kernel herding belongs to a family of deterministic quadratures that seek to minimize the maximum mean discrepancy (MMD), that is, the worst-case integration error over a reproducing kernel Hilbert space (RKHS).

These MMD minimization procedures come with strong experimental support, but comparatively less theoretical footing.

In particular, apart from recent progress in distribution compression, little has been proved in favor of an improvement of MMD minimization over classical Monte Carlo quadrature when the RKHS is infinite-dimensional.

In this paper, we study a joint probability distribution over quadrature nodes, a tailored Gibbs distribution, whose support intuitively tends to concentrate around MMD minimizers as a temperature parameter is decreased.

Our main contribution is to prove that drawing integration nodes from our distribution does outperform i.i.d Monte Carlo.

While our bounds on the worst-case integration error feature the same rate as i.i.d.

Monte Carlo, we do obtain a tighter concentration inequality as the temperature parameter decreases.

This means smaller confidence intervals as the number of quadrature nodes increases.

While arguably a first step, our results demonstrate that the mathematical toolbox developed around Gibbs measures can help understand to what extent kernel herding and its variants improve on computationally cheaper methods.

There remains the issue of sampling from our Gibbs distribution.

In our numerical experiments, we demonstrate that a simple MCMC chain already yields approximate samples that lead to improved confidence intervals around the target integrals, as supported by our theoretical results.