Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs

TL;DR

This paper proves the consistency of a neural ODE-based sparse grid quadrature scheme for expected value estimation, analyzing its convergence rates and regimes for product and general targets.

Contribution

It introduces a neural ODE approach for transport map learning in quadrature, providing theoretical guarantees and analyzing different target regimes.

Findings

Neural ODE transport maps achieve PAC consistency in quadrature.

Fast convergence rates are established for product targets with diagonal maps.

The method mitigates curse of dimensionality via smoothness and activation order.

Abstract

We prove consistency of a recently proposed scheme that evaluates expected values by composing a learned transport map with Clenshaw--Curtis sparse-grid quadrature on a tractable product source. Our analysis hinges on the structural fact that composition of a $C^k_{\mathrm{mix}}$-regular function -- which carries the fast quadrature rate $m^{-k}(\log m)^{(d-1)(k+1)}$ -- with a $C^1$-diffeomorphism can only be guaranteed to be $C^k_{\mathrm{mix}}$ itself, if the diffeomorphism is diagonal up to a permutation of coordinates. The fast rate is therefore available exclusively for product targets, and the analysis splits into two regimes. In the general regime of arbitrary targets, we learn the transport as the time-one flow of a $\mathrm{ReLU}^{k+1}$-neural ODE trained by maximum likelihood. The resulting flow lies in the isotropic space $C^k$ and yields the rate $m^{-k/d}(\log m)^{(d-1)(k/d+1)}$, with raising the density smoothness $k$ and the matched activation order $k+1$ mitigating the curse of dimensionality at the cost of harder optimization. In the diagonal regime of product targets, the Knothe--Rosenblatt map is itself diagonal and we estimate it pointwise via empirical quantile transport, a lightweight alternative that recovers the full mixed-regularity rate. In both regimes, the resulting LtI estimator is PAC (probably approximately correct) consistent. With high probability the numerical integral approximates the true value to arbitrary accuracy as both the sample size $n$ and the quadrature budget $m$ tend to infinity.