arXiv stat.ML
· Papers
Optimal Recalibration of an Online Predictor
arXiv:2607.19689v1 Announce Type: new Abstract: We study the problem of recalibrating an online predictor [KE17, OKS24]: given an arbitrary "hint" sequence of forecasts, the learner must output new predictions that are calibrated while incurring small excess error relative to the original forecasts, under a proper loss