Develops a nonparametric sparse online algorithm to learn the Koopman operator iteratively via stochastic approximation with explicit complexity control and convergence guarantees in misspecified RKHS settings via conditional mean embeddings.
Nonparamet- ric approximation of conditional expectation opera- tors
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ML 2verdicts
UNVERDICTED 2representative citing papers
Derives adaptive Sobolev-norm learning rates for conditional mean embeddings in misspecified RKHS settings, achieving uniform convergence in some regimes.
citing papers explorer
-
Nonparametric Sparse Online Learning of the Koopman Operator
Develops a nonparametric sparse online algorithm to learn the Koopman operator iteratively via stochastic approximation with explicit complexity control and convergence guarantees in misspecified RKHS settings via conditional mean embeddings.
-
Sobolev Norm Learning Rates for Conditional Mean Embeddings
Derives adaptive Sobolev-norm learning rates for conditional mean embeddings in misspecified RKHS settings, achieving uniform convergence in some regimes.