Instance-Adaptive Online Multiclass Multicalibration
The algorithm adapts its prediction strategy to how the sequence of outcome distributions is actually structured, specifically its affine subspace dimension 's'. This adaptation allows it to achieve significantly better error rates than worst-case bounds on easier instances. It combines swap-regret prediction with discovery mechanisms based on weighted outcome averages.