提出一种无需模型假设的鲁棒外推方法，提升小样本下的预测可靠性。
Conformalised imprecise inference for robust extrapolation under limited data
- 用置信校准与模糊推理结合，生成带不确定性的概率框。
- 在分布偏移下仍保持覆盖率，外推时自动扩大不确定性。
- 适合数据少、需高可靠性的实际场景，如医疗或工业检测。
近年来，不确定性量化越来越强调机器学习中随机性与认知性不确定性的区分，推动更统一框架的发展。然而，尽管已有方法能提供可靠预测，但在超出训练域泛化时往往缺乏严格保证。本文提出一种置信校准的模糊推理框架，实现鲁棒外推，该方法模型无关，可为预测模型增加模糊性和距离感知能力。所提方法生成模糊预测（概率盒），在分布偏移下仍保持有效性，在外推区域自适应扩大不确定性，同时维持覆盖度。在合成数据和基准数据集上的实验表明，相比标准概率方法，该方法在小样本条件下显著提升了鲁棒性和可靠性。
原文摘要 · Abstract (English)
Recent advances in uncertainty quantification increasingly emphasise the distinction between aleatory and epistemic uncertainty in machine learning, motivating the need for more unified frameworks. However, despite much progress in producing reliable predictions, existing methods often lack rigorous guarantees when generalising beyond the training domain. We propose a conformalised imprecise inference framework for robust extrapolation, which is model-agnostic and augments predictive models with imprecision and distance awareness. The proposed approach yields imprecise predictions (probability boxes) that remain valid under distributional shift, maintaining coverage while adaptively expanding uncertainty in extrapolation regimes. Experiments on synthetic and benchmark datasets demonstrate improved robustness and reliable coverage compared to standard probabilistic approaches, particularly under limited data.
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