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 …

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

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) …

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 …

Self-play via online learning is one of the premier ways to solve large-scale two-player zero-
sum games, both in theory and practice. Particularly popular algorithms include optimistic …

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 …

Learning with data streams has attracted much attention in recent decades. Conventional
approaches typically assume that the feature and label of a data item can be timely …

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 …