LLM-Driven Performance-Space Augmentation for Meta-Learning-Based Algorithm Selection

ArXi:2605.09518v1 Announce Type: new Meta-learning for algorithm selection relies on a meta-dataset in which each row corresponds to a supervised learning dataset described by meta-features and labelled with a target value that is associated with algorithm choice (typically, some function of algorithm performance). A persistent limitation is that the number of curated real-world datasets is small, resulting in sparse meta-datasets that constrain meta-learner generalisation.