  In this work, we present a variety of novel information-theoretic
generalization bounds for learning algorithms, from the supersample setting of
Steinke & Zakynthinou (2020)-the setting of the "conditional mutual
information" framework. Our development exploits projecting the loss pair
(obtained from a training instance and a testing instance) down to a single
number and correlating loss values with a Rademacher sequence (and its shifted
variants). The presented bounds include square-root bounds, fast-rate bounds,
including those based on variance and sharpness, and bounds for interpolating
algorithms etc. We show theoretically or empirically that these bounds are
tighter than all information-theoretic bounds known to date on the same
supersample setting.
