  Recently, several universal methods have been proposed for online convex
optimization which can handle convex, strongly convex and exponentially concave
cost functions simultaneously. However, most of these algorithms have been
designed with static regret minimization in mind, but this notion of regret may
not be suitable for changing environments. To address this shortcoming, we
propose a novel and intuitive framework for universal online optimization in
dynamic environments. Unlike existing universal algorithms, our strategy does
not rely on the construction of a set of experts and an accompanying
meta-algorithm. Instead, we show that the problem of dynamic online
optimization can be reduced to a uniclass prediction problem. By leaving the
choice of uniclass loss function in the user's hands, they are able to control
and optimize dynamic regret bounds, which in turn carry over into the original
problem. To the best of our knowledge, this is the first paper proposing a
universal approach with state-of-the-art dynamic regret guarantees even for
general convex cost functions.
