arXiv Machine Learning June 30, 2026 By Yindong Chen, Yiwei Wang, Lulu Kang, Chun Liu

A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel

Read the original on arXiv Machine Learning →

arXiv:2111. 10722v4 Announce Type: replace-cross Abstract: We propose a novel deterministic sampling method, EVI-MMD, to approximate a target distribution $\rho^*$ by minimizing the kernel discrepancy, also known as the Maximum Mean Discrepancy (MMD).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

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