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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2402.01139 |
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| _version_ | 1866909211967881216 |
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| author | Angelopoulos, Anastasios N. Barber, Rina Foygel Bates, Stephen |
| author_facet | Angelopoulos, Anastasios N. Barber, Rina Foygel Bates, Stephen |
| contents | We introduce a method for online conformal prediction with decaying step sizes. Like previous methods, ours possesses a retrospective guarantee of coverage for arbitrary sequences. However, unlike previous methods, we can simultaneously estimate a population quantile when it exists. Our theory and experiments indicate substantially improved practical properties: in particular, when the distribution is stable, the coverage is close to the desired level for every time point, not just on average over the observed sequence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01139 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online conformal prediction with decaying step sizes Angelopoulos, Anastasios N. Barber, Rina Foygel Bates, Stephen Machine Learning Methodology We introduce a method for online conformal prediction with decaying step sizes. Like previous methods, ours possesses a retrospective guarantee of coverage for arbitrary sequences. However, unlike previous methods, we can simultaneously estimate a population quantile when it exists. Our theory and experiments indicate substantially improved practical properties: in particular, when the distribution is stable, the coverage is close to the desired level for every time point, not just on average over the observed sequence. |
| title | Online conformal prediction with decaying step sizes |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2402.01139 |