This paper introduces the notion of upper-linearizable/quadratizable functions, a class that
extends concavity and DR-submodularity in various settings, including monotone and non …

Bandit convex optimisation is a fundamental framework for studying zeroth-order convex
optimisation. These notes cover the many tools used for this problem, including cutting plane …

Projection-free online learning has drawn increasing interest due to its efficiency in solving
high-dimensional problems with complicated constraints. However, most existing projection …

In the framework of online convex optimization, most iterative algorithms require the
computation of projections onto convex sets, which can be computationally expensive. To …

We investigate the problem of distributed online convex optimization with complicated
constraints, in which the projection operation could be the computational bottleneck. To …

To deal with non-stationary online problems with complex constraints, we investigate the
dynamic regret of online Frank-Wolfe (OFW), which is an efficient projection-free algorithm …

In many applications, learning systems are required to process continuous non-stationary
data streams. We study this problem in an online learning framework and propose an …

Projection operations are a typical computation bottleneck in online learning. In this paper,
we enable projection-free online learning within the framework of Online Convex …

Abstract The online Frank-Wolfe (OFW) method has gained much popularity for online
convex optimization due to its projection-free property. Previous studies show that OFW can …

We consider the setting of online convex optimization (OCO) with\textit {exp-concave}
losses. The best regret bound known for this setting is $ O (n\log {} T) $, where $ n $ is the …