Abstract: Gradient Descent Ascent (GDA) methods for min-max optimization problems typically produce oscillatory behavior that can lead to instability, e.g., in bilinear settings. To address this ...

Crossing the San Giorgio Bridge at night feels like stepping into the painted dimension of a masterpiece: the outlines of the buildings reflected in the water, the soft lights, and the silence ...

Abstract: We propose an online learning algorithm tailored for a class of machine learning models within a separable stochastic approximation framework. The central idea of our approach is to exploit ...