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：我们提出了一种在线事后校准方法，称为在线Platt缩放（OPS），该方法将Platt缩放技术与在线逻辑回归相结合。我们证明，OPS能够在独立同分布和非独立同分布设置下平滑适应分布漂移。此外，在最佳Platt缩放模型本身存在校准误差的场景中，我们通过引入一种最近开发的称为校准节拍（calibeating）的技术来增强OPS，使其更具鲁棒性。理论上，我们最终得到的OPS+校准节拍方法能够保证在对抗性结果序列下实现校准。实验上，该方法在多种合成和真实数据集上均有效，无论是否存在分布漂移，且无需超参数调优即可达到优越性能。最后，我们将所有OPS思想扩展到beta缩放方法中。