Neural Entropic Optimal Transport and Gromov–Wasserstein Alignment

Optimal transport (OT) and Gromov-Wasserstein (GW) alignment are powerful frameworks for geometrically driven matching of probability distributions, yet their large-scale usage is hampered by high statistical and computational costs. Entropic regularization has emerged as a promising solution, allowing parametric convergence rates via the plug-in estimator, which can be computed using the Sinkhorn algorithm (or its iterations in the GW case). However, Sinkhorn's $O(n^2)$ time complexity for an $n$-sized dataset becomes prohibitive for modern, massive datasets. In this work, we propose a new computational framework for the entropic OT and GW problems that replaces the Sinkhorn step with a neural network trained via backpropagation on mini-batches. By shifting the computational load from the entire dataset to the mini-batch, our approach enables reliable estimation of both the optimal transport/alignment cost and plan at dataset sizes and dimensions far exceeding those tractable with standard Sinkhorn methods. We derive non-asymptotic error bounds for these estimates, showing they achieve minimax-optimal parametric convergence rates for compactly supported distributions. Numerical experiments confirm the accuracy of our method in high-dimensional, large-sample regimes where Sinkhorn is infeasible.