  We present a more general analysis of $H$-calibration for adversarially
robust classification. By adopting a finer definition of calibration, we can
cover settings beyond the restricted hypothesis sets studied in previous work.
In particular, our results hold for most common hypothesis sets used in machine
learning. We both fix some previous calibration results (Bao et al., 2020) and
generalize others (Awasthi et al., 2021). Moreover, our calibration results,
combined with the previous study of consistency by Awasthi et al. (2021), also
lead to more general $H$-consistency results covering common hypothesis sets.
