Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

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 …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

We study the control of linear dynamical systems with adversarial disturbances, as opposed
to statistical noise. We present an efficient algorithm that achieves nearly-tight regret bounds …

Online learning is a framework for the design and analysis of algorithms that build predictive
models by processing data one at the time. Besides being computationally efficient, online …

We study optimal regret bounds for control in linear dynamical systems under adversarially
changing strongly convex cost functions, given the knowledge of transition dynamics. This …

Abstract The Multi-Armed Bandits (MAB) framework highlights the trade-off between
acquiring new knowledge (Exploration) and leveraging available knowledge (Exploitation) …