Meta-surrogate benchmarking for hyperparameter optimization
Despite the recent progress in hyperparameter optimization (HPO), available benchmarks
that resemble real-world scenarios consist of a few and very large problem instances that …
that resemble real-world scenarios consist of a few and very large problem instances that …
The gaussian process autoregressive regression model (gpar)
Multi-output regression models must exploit dependencies between outputs to maximise
predictive performance. The application of Gaussian processes (GPs) to this setting typically …
predictive performance. The application of Gaussian processes (GPs) to this setting typically …
Volatility based kernels and moving average means for accurate forecasting with gaussian processes
A broad class of stochastic volatility models are defined by systems of stochastic differential
equations, and while these models have seen widespread success in domains such as …
equations, and while these models have seen widespread success in domains such as …
Fast transfer Gaussian process regression with large-scale sources
In transfer learning, we aim to improve the predictive modeling of a target output by using the
knowledge from some related source outputs. In real-world applications, the data from the …
knowledge from some related source outputs. In real-world applications, the data from the …
Large scale multi-output multi-class classification using Gaussian processes
C Ma, MA Álvarez - Machine Learning, 2023 - Springer
Abstract Multi-output Gaussian processes (MOGPs) can help to improve predictive
performance for some output variables, by leveraging the correlation with other output …
performance for some output variables, by leveraging the correlation with other output …