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Kernel-based potential mean-field games with unbiased random Fourier $U$-statistics

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arXiv:2605. 29371v1 Announce Type: cross Abstract: We study the subclass of potential mean-field games in which the running interaction cost and the terminal target cost are both expressed through reproducing-kernel maximum mean discrepancy (MMD) penalties, and develop a computational framework that exploits this kernel structure.

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Authors: Yumiharu Nakano
Released: 2026-05-29 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Kernel-based potential mean-field games with unbiased random Fourier $U$-statistics overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, under, line, rate, control, trained. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.29371

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