  We consider the framework of non-stationary Online Convex Optimization where
a learner seeks to control its dynamic regret against an arbitrary sequence of
comparators. When the loss functions are strongly convex or exp-concave, we
demonstrate that Strongly Adaptive (SA) algorithms can be viewed as a
principled way of controlling dynamic regret in terms of path variation $V_T$
of the comparator sequence. Specifically, we show that SA algorithms enjoy
$\tilde O(\sqrt{TV_T} \vee \log T)$ and $\tilde O(\sqrt{dTV_T} \vee d\log T)$
dynamic regret for strongly convex and exp-concave losses respectively without
apriori knowledge of $V_T$. The versatility of the principled approach is
further demonstrated by the novel results in the setting of learning against
bounded linear predictors and online regression with Gaussian kernels.
  Under a related setting, the second component of the paper addresses an open
question posed by Zhdanov and Kalnishkan (2010) that concerns online kernel
regression with squared error losses. We derive a new lower bound on a certain
penalized regret which establishes the near minimax optimality of online Kernel
Ridge Regression (KRR). Our lower bound can be viewed as an RKHS extension to
the lower bound derived in Vovk (2001) for online linear regression in finite
dimensions.
