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 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 …

In this paper, we consider a time-varying optimization approach to the problem of tracking a
moving target using noisy time-of-arrival (TOA) measurements. Specifically, we formulate the …

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 …

We consider a generalization of perturbed best response dynamics in population games
with a continuum of strategies. The previous literature has considered the logit dynamic …

We propose a hierarchical version of dual averaging for zeroth-order online non-convex
optimization {–} ie, learning processes where, at each stage, the optimizer is facing an …

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 …

We examine the long-run behavior of a wide range of dynamics for learning in nonatomic
games, in both discrete and continuous time. The class of dynamics under consideration …

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 …

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 …