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 …

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) …

Online convex optimization (OCO) with arbitrary delays, in which gradients or other
information of functions could be arbitrarily delayed, has received increasing attention …

Making decisions in real-time by learning patterns in an online data-stream is an important
problem in modern machine learning (ML). Applications that fall under this umbrella include …

Deep learning, despite its broad applicability, grapples with robustness challenges in real-
world applications, especially when training and test distributions differ. Reasons for the …