  Existing calibration algorithms address the problem of covariate shift via
unsupervised domain adaptation. However, these methods suffer from the
following limitations: 1) they require unlabeled data from the target domain,
which may not be available at the stage of calibration in real-world
applications and 2) their performances heavily depend on the disparity between
the distributions of the source and target domains. To address these two
limitations, we present novel calibration solutions via domain generalization
which, to the best of our knowledge, are the first of their kind. Our core idea
is to leverage multiple calibration domains to reduce the effective
distribution disparity between the target and calibration domains for improved
calibration transfer without needing any data from the target domain. We
provide theoretical justification and empirical experimental results to
demonstrate the effectiveness of our proposed algorithms. Compared against the
state-of-the-art calibration methods designed for domain adaptation, we observe
a decrease of 8.86 percentage points in expected calibration error,
equivalently an increase of 35 percentage points in improvement ratio, for
multi-class classification on the Office-Home dataset.
