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/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf
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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.