Online conformal prediction with decaying step sizes
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909211967881216 |
|---|---|
| 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 |