  The adaptive gradient online learning method known as AdaGrad has seen
widespread use in the machine learning community in stochastic and adversarial
online learning problems and more recently in deep learning methods. The
method's full-matrix incarnation offers much better theoretical guarantees and
potentially better empirical performance than its diagonal version; however,
this version is computationally prohibitive and so the simpler diagonal version
often is used in practice. We introduce a new method, CompAdaGrad, that
navigates the space between these two schemes and show that this method can
yield results much better than diagonal AdaGrad while avoiding the (effectively
intractable) $O(n^3)$ computational complexity of full-matrix AdaGrad for
dimension $n$. CompAdaGrad essentially performs full-matrix regularization in a
low-dimensional subspace while performing diagonal regularization in the
complementary subspace. We derive CompAdaGrad's updates for composite mirror
descent in case of the squared $\ell_2$ norm and the $\ell_1$ norm, demonstrate
that its complexity per iteration is linear in the dimension, and establish
guarantees for the method independent of the choice of composite regularizer.
Finally, we show preliminary results on several datasets.

Machine Learning;Machine Learning