ATPF learns neural transformations of prior particles by minimizing a kernel maximum mean discrepancy against a particle-filter posterior estimate, with optimal transport regularization and finite-sample generalization bounds.
Importance sampling: Intrinsic dimension and computational cost
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Adversarial Transform Particle Filters
ATPF learns neural transformations of prior particles by minimizing a kernel maximum mean discrepancy against a particle-filter posterior estimate, with optimal transport regularization and finite-sample generalization bounds.