We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that OPS smoothly adapts between i.i.d. and non-i.i.d. settings with distribution drift. Further, in scenarios where the best Platt scaling model is itself miscalibrated, we enhance OPS by incorporating a recently developed technique called calibeating to make it more robust. Theoretically, our resulting OPS+calibeating method is guaranteed to be calibrated for adversarial outcome sequences. Empirically, it is effective on a range of synthetic and real-world datasets, with and without distribution drifts, achieving superior performance without hyperparameter tuning. Finally, we extend all OPS ideas to the beta scaling method.

翻译：我们提出一种在线后处理校准方法，称为在线普拉特缩放（Online Platt Scaling, OPS），该方法将普拉特缩放技术与在线逻辑回归相结合。我们证明OPS能在独立同分布与存在分布漂移的非独立同分布设置之间平滑自适应。此外，当最优普拉特缩放模型本身存在校准偏差时，我们通过引入近期开发的校准增强（calibeating）技术来增强OPS，使其更具鲁棒性。理论上，我们提出的OPS+校准增强方法对于对抗性结果序列具有校准保证。实验上，该方法在多种合成数据集和真实世界数据集（含或不含分布漂移）上均表现有效，无需超参数调优即可达到优越性能。最后，我们将OPS的所有思想扩展到beta缩放方法。