Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.15824 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915559810007040 |
|---|---|
| author | Principato, Guillaume Stoltz, Gilles |
| author_facet | Principato, Guillaume Stoltz, Gilles |
| contents | We study conformal inference in non-exchangeable environments through the lens of Blackwell's theory of approachability. We first recast adaptive conformal inference (ACI, Gibbs and Candès, 2021) as a repeated two-player vector-valued finite game and characterize attainable coverage--efficiency tradeoffs. We then construct coverage and efficiency objectives under potential restrictions on the adversary's play, and design a calibration-based approachability strategy to achieve these goals. The resulting algorithm enjoys strong theoretical guarantees and provides practical insights, though its computational burden may limit deployment in practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15824 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Blackwell's Approachability for Sequential Conformal Inference Principato, Guillaume Stoltz, Gilles Machine Learning 91A26 62G08 We study conformal inference in non-exchangeable environments through the lens of Blackwell's theory of approachability. We first recast adaptive conformal inference (ACI, Gibbs and Candès, 2021) as a repeated two-player vector-valued finite game and characterize attainable coverage--efficiency tradeoffs. We then construct coverage and efficiency objectives under potential restrictions on the adversary's play, and design a calibration-based approachability strategy to achieve these goals. The resulting algorithm enjoys strong theoretical guarantees and provides practical insights, though its computational burden may limit deployment in practice. |
| title | Blackwell's Approachability for Sequential Conformal Inference |
| topic | Machine Learning 91A26 62G08 |
| url | https://arxiv.org/abs/2510.15824 |