Machine learning is one of the fastest growing areas of computer science, with far-reaching
applications. The aim of this textbook is to introduce machine learning, and the algorithmic …

Online learning is a well established learning paradigm which has both theoretical and
practical appeals. The goal of online learning is to make a sequence of accurate predictions …

We present a new family of subgradient methods that dynamically incorporate knowledge of
the geometry of the data observed in earlier iterations to perform more informative gradient …

In this paper, we study online convex optimization in dynamic environments, and aim to
bound the dynamic regret with respect to any sequence of comparators. Existing work have …

Recent literature on online learning has focused on developing adaptive algorithms that
take advantage of a regularity of the sequence of observations, yet retain worst-case …

We study the problem of meta-learning through the lens of online convex optimization,
developing a meta-algorithm bridging the gap between popular gradient-based meta …

We investigate online convex optimization in non-stationary environments and choose the
dynamic regret as the performance measure, defined as the difference between cumulative …

We introduce a new online convex optimization algorithm that adaptively chooses its
regularization function based on the loss functions observed so far. This is in contrast to …

We prove that many mirror descent algorithms for online convex optimization (such as online
gradient descent) have an equivalent interpretation as follow-the-regularized-leader (FTRL) …

We study the online convex optimization problem, in which an online algorithm has to make
repeated decisions with convex loss functions and hopes to achieve a small regret. We …