Multi-objective hyperparameter optimization with performance uncertainty. (arXiv:2209.04340v1 [cs.LG])

The performance of any Machine Learning (ML) algorithm is impacted by the
choice of its hyperparameters. As training and evaluating a ML algorithm is
usually expensive, the hyperparameter optimization (HPO) method needs to be
computationally efficient to be useful in practice. Most of the existing
approaches on multi-objective HPO use evolutionary strategies and
metamodel-based optimization. However, few methods have been developed to
account for uncertainty in the performance measurements. This paper presents
results on multi-objective hyperparameter optimization with uncertainty on …

arxiv optimization performance uncertainty