Convergence of empirical Gromov-Wasserstein distance

Abstract: We study rates of convergence for estimation of the Gromov-Wasserstein distance. For two marginals supported on compact subsets of $\R^{d_x}$ and $\R^{d_y}$, respectively, with $\min { d_x,d_y } > 4$, prior work established the rate $n^{-\frac{2}{\min{d_x,d_y}}}$ for the plug-in empirical estimator based on $n$ i.i.d. samples. We extend this fundamental result to marginals with unbounded supports, assuming only finite polynomial moments. Our proof techniques for the upper bounds can be adapted to obtain sample complexity results for penalized Wasserstein alignment that encompasses the Gromov-Wasserstein distance and Wasserstein Procrustes in unbounded settings. Furthermore, we establish matching minimax lower bounds (up to logarithmic factors) for estimating the Gromov-Wasserstein distance.