2024/10/17 by Zheng, Minxing, Shixiang Zhu, Zhu, Shixiang · 2 citations
Computer Science · Mathematics · Psychology · #Computer science #Conformal map #Conformity #Econometrics #FOS: Computer and information sciences #Face and Expression Recognition #Gaussian Processes and Bayesian Inference #Geometry #Machine Learning (cs.LG) #Mathematics #Methodology (stat.ME) #Neural Networks and Applications #Probabilistic logic #Psychology #Social psychology #Statistics
paper · pdf · doi:10.48550/arxiv.2410.13735
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/10/17 · openalex created_date 2024/10/21 · openalex updated_date 2026/08/05
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.