  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.
