  Predictive models in ML need to be trustworthy and reliable, which often at
the very least means outputting calibrated probabilities. This can be
particularly difficult to guarantee in the online prediction setting when the
outcome sequence can be generated adversarially. In this paper we introduce a
technique using Blackwell's approachability theorem for taking an online
predictive model which might not be calibrated and transforming its predictions
to calibrated predictions without much increase to the loss of the original
model. Our proposed algorithm achieves calibration and accuracy at a faster
rate than existing techniques arXiv:1607.03594 and is the first algorithm to
offer a flexible tradeoff between calibration error and accuracy in the online
setting. We demonstrate this by characterizing the space of jointly achievable
calibration and regret using our technique.
