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，称为在线普拉特缩放（OPS），它将普拉特缩放技术与在线逻辑回归相结合。我们展示了OPS在i.i.d.和非i.i.d.设置中具有平滑的自适应性，并适用于分布漂移。此外，在最佳普拉特缩放模型本身被误校准的情况下，我们增加了最近开发的称为calibeating的技术来增强OPS的稳健性。理论上，我们的结果OPS + calibeating方法保证在对抗性结果序列的情况下进行校准。在一系列合成和实际数据集上，即使没有进行超参数调整，它也具有卓越的性能，并且在有分布漂移和没有分布漂移的情况下都是有效的。最后，我们将所有OPS思想扩展到beta缩放方法中。