提出无需调参的自适应置信推断方法，提升真实场景下模型不确定性估计可靠性。
Adaptive Conformal Inference by Betting
- 采用无参数在线凸优化技术替代传统梯度下降，避免学习率敏感问题。
- 理论保证长期误覆盖频率控制在预设水平，实测表现稳定优异。
- 适合对模型不确定性有严格要求且不愿调参的工程落地场景。
置信推断是量化机器学习模型预测不确定性的有力工具，但其有效性依赖于数据可交换性假设，该假设在真实场景中常不成立。本文研究无需任何数据生成过程假设的自适应置信推断问题。现有方法基于在线梯度下降优化分位数损失，但对学习率选择高度敏感且需繁琐调参。本文提出一种新方法，利用无参数在线凸优化技术实现自适应置信推断。理论证明该方法能将长期误覆盖频率控制在名义水平，实验证明其性能出色且无需复杂参数调整。
原文摘要 · Abstract (English)
Conformal prediction is a valuable tool for quantifying predictive uncertainty of machine learning models. However, its applicability relies on the assumption of data exchangeability, a condition which is often not met in real-world scenarios. In this paper, we consider the problem of adaptive conformal inference without any assumptions about the data generating process. Existing approaches for adaptive conformal inference are based on optimizing the pinball loss using variants of online gradient descent. A notable shortcoming of such approaches is in their explicit dependence on and sensitivity to the choice of the learning rates. In this paper, we propose a different approach for adaptive conformal inference that leverages parameter-free online convex optimization techniques. We prove that our method controls long-term miscoverage frequency at a nominal level and demonstrate its convincing empirical performance without any need of performing cumbersome parameter tuning.
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