The fast adaptation capability of deep neural networks in non-stationary environments is
critical for online time series forecasting. Successful solutions require handling changes to …

Adaptive optimization methods, such as AdaGrad, RMSProp, and Adam, are widely used in
solving large-scale machine learning problems. A number of schemes have been proposed …

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 …

The Gaussian homotopy (GH) method is a popular approach to finding better stationary
points for non-convex optimization problems by gradually reducing a parameter value $ t …

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 …

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 …

In this paper, we find that existing online forecasting methods have the following issues: 1)
They do not consider the update frequency of streaming data and directly use labels (future …

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 …

Abstract While Online Gradient Descent and other no-regret learning procedures are known
to efficiently converge to a coarse correlated equilibrium in games where each agent's utility …

Demand forecasting is crucial for the operation and planning of the energy and power
industry. Accurate demand forecasting can assist decision-makers in reducing operational …