  The hypothesis that sub-network initializations (lottery) exist within the
initializations of over-parameterized networks, which when trained in isolation
produce highly generalizable models, has led to crucial insights into network
initialization and has enabled efficient inferencing. Supervised models with
uncalibrated confidences tend to be overconfident even when making wrong
prediction. In this paper, for the first time, we study how explicit confidence
calibration in the over-parameterized network impacts the quality of the
resulting lottery tickets. More specifically, we incorporate a suite of
calibration strategies, ranging from mixup regularization, variance-weighted
confidence calibration to the newly proposed likelihood-based calibration and
normalized bin assignment strategies. Furthermore, we explore different
combinations of architectures and datasets, and make a number of key findings
about the role of confidence calibration. Our empirical studies reveal that
including calibration mechanisms consistently lead to more effective lottery
tickets, in terms of accuracy as well as empirical calibration metrics, even
when retrained using data with challenging distribution shifts with respect to
the source dataset.
