We present new algorithms for optimizing non-smooth, non-convex stochastic objectives
based on a novel analysis technique. This improves the current best-known complexity for …

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

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 …

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

We consider the problem of nonstochastic control with a sequence of quadratic losses, ie,
LQR control. We provide an efficient online algorithm that achieves an optimal dynamic …

Online gradient descent (OGD) is well-known to be doubly optimal under strong convexity or
monotonicity assumptions:(1) in the single-agent setting, it achieves an optimal regret of Θ …

This paper focuses on supervised and unsupervised online label shift, where the class
marginals $ Q (y) $ variesbut the class-conditionals $ Q (x| y) $ remain invariant. In the …

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 …