On the Laplace Approximation as Model Selection Criterion for Gaussian Processes

arXiv:2403.09215v1 Announce Type: new
Abstract: Model selection aims to find the best model in terms of accuracy, interpretability or simplicity, preferably all at once. In this work, we focus on evaluating model performance of Gaussian process models, i.e. finding a metric that provides the best trade-off between all those criteria. While previous work considers metrics like the likelihood, AIC or dynamic nested sampling, they either lack performance or have significant runtime issues, which severely limits applicability. We address these challenges …

abstract accuracy approximation arxiv criterion cs.ai cs.lg focus gaussian processes interpretability laplace approximation model selection performance process processes simplicity terms trade trade-off type work