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.
Empirical processes associated with v-statistics and a class of estimators under random censoring
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
stat.ME 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
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.