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

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 study the classic online learning problem of predicting with expert advice, and propose a
truly parameter-free and adaptive algorithm that achieves several objectives simultaneously …

We study online aggregation of the predictions of experts, and first show new second-order
regret bounds in the standard setting, which are obtained via a version of the Prod algorithm …

A recent line of work has shown a surprising connection between multicalibration, a multi-
group fairness notion, and omniprediction, a learning paradigm that provides simultaneous …

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

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 aim to design strategies for sequential decision making that adjust to the difficulty of the
learning problem. We study this question both in the setting of prediction with expert advice …

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 …

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 …