On the Optimisation Landscape of Overparameterised Weighted MMD

Abstract:We study the optimisation landscape of the maximum mean discrepancy (MMD) over weighted atomic measures. For the one-dimensional Gaussian kernel, we show that, for any compactly supported target distribution, sufficiently many particles ensure that every local minimum has arbitrarily small MMD. Moreover, any stationary point whose MMD remains bounded away from zero is unstable under an unweighted gradient flow over the particle locations. These results hold both when the weights are constrained to the simplex, and when they are unconstrained but optimised at each step. When the target is itself finitely supported, we further strengthen this result: beyond an explicit particle threshold, there are no spurious local minima, and every spurious stationary point is unstable. To our knowledge, these are the first finite-particle overparameterisation guarantees of their kind for weighted MMD optimisation.

Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2610.19861 [stat.ML]

(or arXiv:2610.19861v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.19861 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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