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

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

We consider the problem of the Zinkevich (2003)-style dynamic regret minimization in online
learning with\emph {exp-concave} losses. We show that whenever improper learning is …

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

We study the framework of universal dynamic regret minimization with strongly convex
losses. We answer an open problem in Baby and Wang 2021 by showing that in a proper …

We study the problem of Online Convex Optimization (OCO) with memory, which allows loss
functions to depend on past decisions and thus captures temporal effects of learning …

Dealing with distribution shifts is one of the central challenges for modern machine learning.
One fundamental situation is the covariate shift, where the input distributions of data change …

Abstract We investigate online Markov Decision Processes (MDPs) with adversarially
changing loss functions and known transitions. We choose dynamic regret as the …

Motivated by the challenge of nonstationarity in sequential decision making, we study Online
Convex Optimization (OCO) under the coupling of two problem structures: the domain is …

In this paper, we present an improved analysis for dynamic regret of strongly convex and
smooth functions. Specifically, we investigate the Online Multiple Gradient Descent (OMGD) …