让回归模型直接学出可靠且窄的置信区间，一次训练多用。
SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors
- 通过可微损失联合优化区间精度与覆盖率，无需分批或预设置信度。
- 在多个置信水平下保持有效性，区间比传统方法更紧，计算成本更低。
- 适合需要高效不确定性估计的实时预测场景，如医疗、自动驾驶。
置信预测（CP）为预测模型提供可靠的不确定性保证，但通常在训练后应用，导致训练目标与生成紧凑预测区间的初衷不一致。本文提出SPACR（单遍自适应置信回归器），一种可在可微损失中直接训练不确定性感知回归器的新方法。SPACR在训练过程中联合优化效率与有效性，无需批量分割或预先设定置信水平。因此，单个SPACR模型在推理时可生成多个置信水平下的有效预测区间，避免了DOICR等方法所需的昂贵重训练。在多种数据集上的实验表明，SPACR在保持良好覆盖性的同时，始终产生更紧的区间，显著降低计算开销。
原文摘要 · Abstract (English)
Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model training with the conformal goal of producing efficient (i.e, narrow) intervals. We propose SPACR (Single-Pass Adaptive Conformal Regressor), a novel method for directly training uncertainty-aware regressors within a differentiable loss. SPACR jointly optimizes efficiency and validity without batch-splitting or a predefined confidence levels during training. As a result, a single SPACR model yields valid prediction intervals at multiple confidence levels during inference, avoiding the costly retraining required by methods like DOICR. Experiments on diverse datasets show that SPACR consistently gives tighter intervals and better coverage-efficiency trade-offs compared to standard CP and DOICR, while significantly reducing computational costs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。