We investigate online nonconvex optimization from a local regret minimization perspective.
Previous studies along this line implicitly required the access to sufficient gradient oracles at …

This paper develops a methodology for regret minimization with stochastic first-order oracle
feedback in online, constrained, non-smooth, non-convex problems. In this setting, the …

This paper introduces\textit {online bilevel optimization} in which a sequence of time-varying
bilevel problems is revealed one after the other. We extend the known regret bounds for …

We propose a new family of adaptive first-order methods for a class of convex minimization
problems that may fail to be Lipschitz continuous or smooth in the standard sense …

Bilevel optimization has become a powerful tool in a wide variety of machine learning
problems. However, the current nonconvex bilevel optimization considers an offline dataset …

Online nonconvex optimization has been an active area of research recently. Previous
studies either considered the global regret with full information about the objective functions …

In many online decision processes, the optimizing agent is called to choose between large
numbers of alternatives with many inherent similarities; in turn, these similarities imply …

In stochastic Nash equilibrium problems (SNEPs), it is natural for players to be uncertain
about their complex environments and have multi-dimensional unknown parameters in their …

We consider distributed online min-max resource allocation with a set of parallel agents and
a parameter server. Our goal is to minimize the pointwise maximum over a set of time …

Trilevel learning (TLL) found diverse applications in numerous machine learning
applications, ranging from robust hyperparameter optimization to domain adaptation …