BOO achieves exponentially decaying regret O(N^{-√N}) by combining Bayesian optimisation and partitioning-based optimistic optimisation for Matérn GP functions with ν > 4 + D/2.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
method 1polarities
use method 1representative citing papers
Two-dimensional filtering of spatially uncorrelated white noise generates red along-track spectra in SWOT observations, matching observed power-law behavior at small scales.
Neural feature maps create expressive kernels that enable fast, scalable, and consistent exact Gaussian process inference for regression and classification.
citing papers explorer
-
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
BOO achieves exponentially decaying regret O(N^{-√N}) by combining Bayesian optimisation and partitioning-based optimistic optimisation for Matérn GP functions with ν > 4 + D/2.
-
The impact of two-dimensional filtering on white noise spectra in SWOT along-track observations
Two-dimensional filtering of spatially uncorrelated white noise generates red along-track spectra in SWOT observations, matching observed power-law behavior at small scales.
-
Scalable Gaussian process inference via neural feature maps
Neural feature maps create expressive kernels that enable fast, scalable, and consistent exact Gaussian process inference for regression and classification.