A Gradient Flow Perspective on Minimum MMD Estimation
Minimum maximum mean discrepancy (MMD) estimation has emerged as a robust and likelihood-free alternative to maximum likelihood estimation for parameter estimation. Yet, despite its practical success, the associated optimization problem remains poorly understood, with theoretical guarantees for existing algorithms hinging on convexity assumptions that rarely hold in practice. We address this gap by proposing a preconditioned gradient descent (PGD) scheme, establishing its asymptotic \emph{global} convergence under explicit gradient-dominance and projection-residual conditions. Our approach is inspired by recent progress on MMD gradient flows, a nonparametric descent scheme on the space of...
| Authors | Sophia Seulkee Kang, Louis Sharrock, Xiaoyuan Cheng et al. |
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| Primary category | cs.LG |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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