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

Stochastic nested optimization, including stochastic compositional, min-max, and bilevel
optimization, is gaining popularity in many machine learning applications. While the three …

Generating images from natural language instructions is an intriguing yet highly challenging
task. We approach text-to-image generation by combining the power of the retrained CLIP …

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

Stochastic bilevel optimization generalizes the classic stochastic optimization from the
minimization of a single objective to the minimization of an objective function that depends …

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 …

Stochastic bilevel optimization generalizes the classic stochastic optimization from the
minimization of a single objective to the minimization of an objective function that depends …

Peer-to-peer (P2P) energy trading is a promising market scheme to accommodate the
increasing distributed energy resources (DERs). However, how P2P to be integrated into the …

Bilevel optimization is a special type of optimization in which one problem is embedded
within another. The bilevel optimization problem (BLOP) of which both levels are …

This review discusses methods for learning parameters for image reconstruction problems
using bilevel formulations. Image reconstruction typically involves optimizing a cost function …