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

arXiv:2605.29371 · math.OC, cs.LG, cs.NA, math.NA, stat.ML · Submitted 2026-05-28 · Read on arXiv

math.OC, cs.LG, cs.NA, math.NA, stat.ML

Submitted: 2026-05-28

Updated: 2026-09-20

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Consistency — 8× in this paper · explained in 3 episodes
• Optimal Transport — 7× in this paper · explained in 5 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Hilbert Space — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Curse of Dimensionality — 3× in this paper · explained in 6 episodes
• Convergence Guarantees — 2× in this paper · explained in 3 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features
• On the convergence rates of generalized conditional gradient method for fully discretized Mean Field Games
• On the Generalized Conditional Gradient Method for Mean Field Games with Local Coupling Terms
• A kernel-based method for Schr\"odinger bridges
• Solving Monge problem by Hilbert space embeddings of probability measures

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