Aggregation in conformal e-classification
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917473294483456 |
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| author | Vovk, Vladimir |
| author_facet | Vovk, Vladimir |
| contents | Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least approximately. An important advantage of conformal e-predictors is that they are easier to aggregate without sacrificing their validity. This paper studies experimentally cross-conformal e-prediction, which is an existing method of aggregating conformal e-predictors, and its modifications that are conceptually simpler and more flexible. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07963 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Aggregation in conformal e-classification Vovk, Vladimir Machine Learning 68Q32 (Primary) 62G15, 68T05 (Secondary) I.2.6 Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least approximately. An important advantage of conformal e-predictors is that they are easier to aggregate without sacrificing their validity. This paper studies experimentally cross-conformal e-prediction, which is an existing method of aggregating conformal e-predictors, and its modifications that are conceptually simpler and more flexible. |
| title | Aggregation in conformal e-classification |
| topic | Machine Learning 68Q32 (Primary) 62G15, 68T05 (Secondary) I.2.6 |
| url | https://arxiv.org/abs/2605.07963 |