A kernel framework over parameter space yields confidence bounds for regularized nonlinear models on adaptive data, supporting convergence analysis in Bayesian optimization.
URL https://proceedings.neurips.cc/paper_files/paper/2011/ file/e1d5be1c7f2f456670de3d53c7b54f4a-Paper.pdf
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
A kernel framework over parameter space yields confidence bounds for regularized nonlinear models on adaptive data, supporting convergence analysis in Bayesian optimization.