Generative Conformal Prediction with Vectorized Non-Conformity Scores
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909487383707648 |
|---|---|
| author | Zheng, Minxing Zhu, Shixiang |
| author_facet | Zheng, Minxing Zhu, Shixiang |
| contents | Conformal prediction (CP) provides model-agnostic uncertainty quantification with guaranteed coverage, but conventional methods often produce overly conservative uncertainty sets, especially in multi-dimensional settings. This limitation arises from simplistic non-conformity scores that rely solely on prediction error, failing to capture the prediction error distribution's complexity. To address this, we propose a generative conformal prediction framework with vectorized non-conformity scores, leveraging a generative model to sample multiple predictions from the fitted data distribution. By computing non-conformity scores across these samples and estimating empirical quantiles at different density levels, we construct adaptive uncertainty sets using density-ranked uncertainty balls. This approach enables more precise uncertainty allocation -- yielding larger prediction sets in high-confidence regions and smaller or excluded sets in low-confidence regions -- enhancing both flexibility and efficiency. We establish theoretical guarantees for statistical validity and demonstrate through extensive numerical experiments that our method outperforms state-of-the-art techniques on synthetic and real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13735 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Conformal Prediction with Vectorized Non-Conformity Scores Zheng, Minxing Zhu, Shixiang Machine Learning Methodology Conformal prediction (CP) provides model-agnostic uncertainty quantification with guaranteed coverage, but conventional methods often produce overly conservative uncertainty sets, especially in multi-dimensional settings. This limitation arises from simplistic non-conformity scores that rely solely on prediction error, failing to capture the prediction error distribution's complexity. To address this, we propose a generative conformal prediction framework with vectorized non-conformity scores, leveraging a generative model to sample multiple predictions from the fitted data distribution. By computing non-conformity scores across these samples and estimating empirical quantiles at different density levels, we construct adaptive uncertainty sets using density-ranked uncertainty balls. This approach enables more precise uncertainty allocation -- yielding larger prediction sets in high-confidence regions and smaller or excluded sets in low-confidence regions -- enhancing both flexibility and efficiency. We establish theoretical guarantees for statistical validity and demonstrate through extensive numerical experiments that our method outperforms state-of-the-art techniques on synthetic and real-world datasets. |
| title | Generative Conformal Prediction with Vectorized Non-Conformity Scores |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2410.13735 |