The main goal of machine learning is the creation of self-learning algorithms in many areas
of human activity. It allows a replacement of a person with artificial intelligence in seeking to …

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 …

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

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

Bilevel optimization has been widely applied in many important machine learning
applications such as hyperparameter optimization and meta-learning. Recently, several …

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

Bilevel optimization, the problem of minimizing a value function which involves the arg-
minimum of another function, appears in many areas of machine learning. In a large scale …

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

The conflicting gradients problem is one of the major bottlenecks for the effective training of
machine learning models that deal with multiple objectives. To resolve this problem, various …

Bilevel optimization have gained growing interests, with numerous applications found in
meta learning, minimax games, reinforcement learning, and nested composition …