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 …

Enhancing images in low-light scenes is a challenging but widely concerned task in the
computer vision. The mainstream learning-based methods mainly acquire the enhanced …

Bilevel optimization (BO) is useful for solving a variety of important machine learning
problems including but not limited to hyperparameter optimization, meta-learning, continual …

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

Bilevel optimization enjoys a wide range of applications in hyper-parameter optimization,
meta-learning and reinforcement learning. However, bilevel problems are difficult to solve …

Bilevel optimization has found extensive applications in modern machine learning problems
such as hyperparameter optimization, neural architecture search, meta-learning, etc. While …

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 …

Infrared and visible image fusion is a powerful technique that combines complementary
information from different modalities for downstream semantic perception tasks. Existing …