In this paper, we propose an online convex optimization approach with two different levels of
adaptivity. On a higher level, our approach is agnostic to the unknown types and curvatures …

The stochastically extended adversarial (SEA) model, introduced by Sachs et al.(2022),
serves as an interpolation between stochastic and adversarial online convex optimization …

Multi-player online games depict the interaction of multiple players with each other over
time. Strongly monotone games are of particular interest since they have benign properties …

To deal with changing environments, a new performance measure—adaptive regret, defined
as the maximum static regret over any interval, was proposed in online learning. Under the …

We develop universal gradient methods for Stochastic Convex Optimization (SCO). Our
algorithms automatically adapt not only to the oracle's noise but also to the H\" older …

This work focuses on dynamic regret for non-stationary online convex optimization with full
information. State-of-the-art analysis shows that Implicit Online Mirror Descent (IOMD) …

To address the uncertainty in function types, recent progress in online convex optimization
(OCO) has spurred the development of universal algorithms that simultaneously attain …

The trade-off between regret and computational cost is a fundamental problem for online
kernel regression, and previous algorithms worked on the trade-off can not keep optimal …

In the field of online convex optimization, some efficient algorithms have been designed for
each of the individual classes of objective functions, eg, convex, strongly convex, and exp …

In this paper, we explore online convex optimization (OCO) and introduce a new analysis
that provides fast rates by exploiting the curvature of feasible sets. In online linear …