Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in
learning fields. The validity of existing works heavily rely on either a restrictive Lower-Level …

We consider stochastic unconstrained bilevel optimization problems when only the first-
order gradient oracles are available. While numerous optimization methods have been …

We introduce contextual stochastic bilevel optimization (CSBO)--a stochastic bilevel
optimization framework with the lower-level problem minimizing an expectation conditioned …

In this paper, we propose a novel Hessian inverse free Fully Single Loop Algorithm (FSLA)
for bilevel optimization problems. Classic algorithms for bilevel optimization admit a double …

Recently, bilevel optimization (BLO) has taken center stage in some very exciting
developments in the area of signal processing (SP) and machine learning (ML). Roughly …

In this paper, we study a class of stochastic bilevel optimization problems, also known as
stochastic simple bilevel optimization, where we minimize a smooth stochastic objective …

Bilevel optimization has arisen as a powerful tool for solving a variety of machine learning
problems. Two current popular bilevel optimizers AID-BiO and ITD-BiO naturally involve …

Functional constrained optimization (FCO) has emerged as a powerful tool for solving
various machine learning problems. However, with the rapid increase in applications of …

Federated bilevel optimization (FBO) has shown great potential recently in machine learning
and edge computing due to the emerging nested optimization structure in meta-learning …

Deep learning has achieved remarkable success in many machine learning tasks such as
image classification, speech recognition, and game playing. However, these breakthroughs …