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.

我们提出了一种名为在线 Platt 校准的后处理校准方法，它将 Platt 校准技术与在线逻辑回归相结合。我们证明 OPS 在连续分布漂移的 i.i.d. 和 non-i.i.d. 场景下能够平滑适应。同时，我们在最优 Platt 校准模型本身被错误校准的情况下，通过引入 calibeating 技术增强 OPS 以使其更加鲁棒。理论上，我们的 OPS+calibeating 方法对于对抗性结果序列是保证校准的。实验结果表明，它在一系列合成和真实世界数据集上均能取得卓越性能，无需超参数调整。最后，我们将所有 OPS 的想法扩展到 beta 校准方法。