Bilevel optimization recently has received tremendous attention due to its great success in
solving important machine learning problems like meta learning, reinforcement learning …

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 …

In this paper, we study a class of unconstrained and constrained bilevel optimization
problems in which the lower level is a possibly nonsmooth convex optimization problem …

We consider stochastic bilevel optimization problems involving minimizing an upper-level
($\texttt {UL} $) function that is dependent on the arg-min of a strongly-convex lower-level …

Federated bilevel learning has received increasing attention in various emerging machine
learning and communication applications. Recently, several Hessian-vector-based …

We present a method for solving general nonconvex-strongly-convex bilevel optimization
problems. Our method---the Restarted Accelerated HyperGradient Descent (RAHGD) …

As hyper-parameters are ubiquitous and can significantly affect the model performance,
hyper-parameter optimization is extremely important in machine learning. In this paper, we …

Bilevel optimization has various applications such as hyper-parameter optimization and
meta-learning. Designing theoretically efficient algorithms for bilevel optimization is more …

Minimax optimization has served as the backbone of many machine learning problems.
Although the convergence behavior of optimization algorithms has been extensively studied …

Two-point zeroth order methods are important in many applications of zeroth-order
optimization arising in robotics, wind farms, power systems, online optimization, and …