Distributed online optimization and online games have been increasingly researched in the
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …

A central capability of intelligent systems is the ability to continuously build upon previous
experiences to speed up and enhance learning of new tasks. Two distinct research …

This work addresses decentralized online optimization in nonstationary environments. A
network of agents aim to track the minimizer of a global, time-varying, and convex function …

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

Learning from repeated play in a fixed two-player zero-sum game is a classic problem in
game theory and online learning. We consider a variant of this problem where the game …

We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal
in terms of dynamic regret. Specifically, our algorithm achieves $\mathcal {O}(\min\{\sqrt …

The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …

This article investigates the distributed online optimization problem over a multi-agent
network subject to local set constraints and coupled inequality constraints, which has a lot of …

Distributed bandit online convex optimization with time-varying coupled inequality
constraints is considered, motivated by a repeated game between a group of learners and …

Recently, there has been a growing research interest in the analysis of dynamic regret,
which measures the performance of an online learner against a sequence of local …