High-velocity streams of high-dimensional data pose significant “big data” analysis
challenges across a range of applications and settings. Online learning and online convex …

To cope with changing environments, recent developments in online learning have
introduced the concepts of adaptive regret and dynamic regret independently. In this paper …

We provide a new online learning algorithm that for the first time combines several disparate
notions of adaptivity. First, our algorithm obtains a “parameter-free” regret bound that adapts …

This paper describes a new online convex optimization method which incorporates a family
of candidate dynamical models and establishes novel tracking regret bounds that scale with …

The fairness-aware online learning framework has emerged as a potent tool within the
context of continuous lifelong learning. In this scenario, the learner's objective is to …

We provide the first oracle efficient sublinear regret algorithms for adversarial versions of the
contextual bandit problem. In this problem, the learner repeatedly makes an action on the …

First-price auctions have very recently swept the online advertising industry, replacing
second-price auctions as the predominant auction mechanism on many platforms. This shift …

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

This paper describes a new parameter-free online learning algorithm for changing
environments. In comparing against algorithms with the same time complexity as ours, we …

To deal with changing environments, adaptive regret has been proposed to minimize the
regret over every interval. Previous studies have established a small-loss adaptive regret …