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 …

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 …

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

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 …

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 …

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 …

We address online learning in complex auction settings, such as sponsored search
auctions, where the value of the bidder is unknown to her, evolving in an arbitrary manner …

Regret minimization is treated as the golden rule in the traditional study of online learning.
However, regret minimization algorithms tend to converge to the static optimum, thus being …

In the problem of online learning for changing environments, data are sequentially received
one after another over time, and their distribution assumptions may vary frequently. Although …