A multi-source extension of constrained Max-value Entropy Search for Bayesian optimization incorporates auxiliary data sources to improve early exploration and performance under constraints even with weak correlations.
Scalable Variational Gaussian Process Classification
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Constrained Bayesian Optimisation with Multiple Information Sources
A multi-source extension of constrained Max-value Entropy Search for Bayesian optimization incorporates auxiliary data sources to improve early exploration and performance under constraints even with weak correlations.