Partial Rankings of Optimizers
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916384098746368 |
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| author | Rodemann, Julian Blocher, Hannah |
| author_facet | Rodemann, Julian Blocher, Hannah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16565 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Partial Rankings of Optimizers Rodemann, Julian Blocher, Hannah Machine Learning 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. |
| title | Partial Rankings of Optimizers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.16565 |