With high penetration of renewable generation and distributed energy resources, distribution
systems are facing new operational challenges due to their intermittency and uncertainty. To …

A bilevel programming model represents the relationship in a specific decision process that
involves decisions within a hierarchical structure of two levels. The upper-level problem is …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Abstract Multi-Class Incremental Learning (MCIL) aims to learn new concepts by
incrementally updating a model trained on previous concepts. However, there is an inherent …

Abstract Class-Incremental Learning (CIL) aims to learn a classification model with the
number of classes increasing phase-by-phase. An inherent problem in CIL is the stability …

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …

This paper presents a new bi-level multi-objective model to valorize the microgrid (MG)
flexibility based on flexible power management system. It considers the presence of …

Deep semi-supervised learning (SSL) has been recently shown very effectively. However, its
performance is seriously decreased when the class distribution is mismatched, among …

Bilevel optimization is a field of mathematical programming in which some variables are
constrained to be the solution of another optimization problem. As a consequence, bilevel …

Standard federated optimization methods successfully apply to stochastic problems with
single-level structure. However, many contemporary ML problems-including adversarial …