arXiv cs.AI· Trung-Khang Tran, Tuan A. Hoang, Viet-Hoang Tran, Tan M. Nguyen·· 11 小时前AI 评分23
面向分布到分布回归的共形预测新框架
Conformal Prediction for Distribution-to-Distribution Regression
AI 导读
针对分布到分布回归（Distribution-to-Distribution Regression）场景，研究者指出直接套用 Split Conformal Prediction 得到的预测集虽满足边际有效性，却缺乏可解释性——没有明确体积、采样过程也难以处理。
正文 · 原文
Abstract:Conformal Prediction (CP) provides a model-agnostic framework for constructing finite-sample prediction sets with coverage guarantees and has become a tool for reliable decision-making in high-risk settings. Recent advances have extended Split Conformal Prediction to increasingly complex learning problems, including multilabel, high-dimensional, and functional outputs. In this work, we study CP for Distribution-to-Distribution Regression, a setting of growing interest across many application domains. We show that a naive application of Split CP yields prediction sets that, while marginally valid, are non-interpretable, e.g., lacking a well-defined volume or a tractable sampling procedure, and of limited practical value. To overcome this, we propose a new conformal framework that operates in an orthogonal basis space, producing interpretable prediction sets while preserving coverage guarantees. We further develop an adaptive variant with asymptotic conditional coverage under mild assumptions. Empirical results on synthetic and real-world datasets validate the effectiveness of the proposed approach. The code is publicly available at this https URL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06320 [cs.LG] |
| (or arXiv:2611.06320v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06320 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Khang Tran [view email]
[v1]
Wed, 4 Nov 2026 10:54:16 UTC (1,081 KB)
来源：arXiv cs.AI · arxiv.org