In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …

This paper notes a simple connection between synthetic control and online learning.
Specifically, we recognize synthetic control as an instance of Follow‐The‐Leader (FTL) …

Abstract Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) control
are foundational and extensively researched problems in optimal control. We investigate …

In this paper, we provide a general framework for studying multi-agent online learning
problems in the presence of delays and asynchronicities. Specifically, we propose and …

We examine the problem of regret minimization when the learner is involved in a continuous
game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is …

In many practical applications of machine learning data arrives sequentially over time in
large chunks. Practitioners have then to decide how to allocate their computational budget in …

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 study nonstochastic bandits and experts in a delayed setting where delays depend on
both time and arms. While the setting in which delays only depend on time has been …

We investigate bandit convex optimization (BCO) with delayed feedback, where only the
loss value of the action is revealed under an arbitrary delay. Previous studies have …

Finding equilibria via gradient play in competitive multi-agent games has been attracting a
growing amount of attention in recent years, with emphasis on designing efficient strategies …