Partial Rankings of Optimizers
<|reference_start|>Partial Rankings of Optimizers: We introduce a framework for benchmarking optimizers according to multiple criteria over various test functions. Based on a recently introduced union-free generic depth function for partial orders/rankings, it fully exploits the ordinal information and allows for incomparability. Our method describes the distribution of all partial orders/rankings, avoiding the notorious shortcomings of aggregation. This permits to identify test functions that produce central or outlying rankings of optimizers and to assess the quality of benchmarking suites.<|reference_end|>
@article{rodemann2024partial,
    title={Partial Rankings of Optimizers},
    author={Julian Rodemann and Hannah Blocher},
    journal={arXiv preprint arXiv:2402.16565},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16565},
    primaryClass={cs.LG stat.ML}
}