Averaged method of multipliers for bi-level optimization without lower-level strong convexity
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 …
learning fields. The validity of existing works heavily rely on either a restrictive Lower-Level …
Bome! bilevel optimization made easy: A simple first-order approach
Bilevel optimization (BO) is useful for solving a variety of important machine learning
problems including but not limited to hyperparameter optimization, meta-learning, continual …
problems including but not limited to hyperparameter optimization, meta-learning, continual …
An Introduction to Bilevel Optimization: Foundations and applications in signal processing and machine learning
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 …
developments in the area of signal processing (SP) and machine learning (ML). Roughly …
Slm: A smoothed first-order lagrangian method for structured constrained nonconvex optimization
S Lu - Advances in Neural Information Processing Systems, 2024 - proceedings.neurips.cc
Functional constrained optimization (FCO) has emerged as a powerful tool for solving
various machine learning problems. However, with the rapid increase in applications of …
various machine learning problems. However, with the rapid increase in applications of …
Non-convex bilevel games with critical point selection maps
Bilevel optimization problems involve two nested objectives, where an upper-level objective
depends on a solution to a lower-level problem. When the latter is non-convex, multiple …
depends on a solution to a lower-level problem. When the latter is non-convex, multiple …
A task-guided, implicitly-searched and metainitialized deep model for image fusion
Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially
for enhancing visual quality and/or extracting aggregated features for perception. However …
for enhancing visual quality and/or extracting aggregated features for perception. However …
Learning with constraint learning: New perspective, solution strategy and various applications
The complexity of learning problems, such as Generative Adversarial Network (GAN) and its
variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world …
variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world …
On momentum-based gradient methods for bilevel optimization with nonconvex lower-level
F Huang - arXiv preprint arXiv:2303.03944, 2023 - arxiv.org
Bilevel optimization is a popular two-level hierarchical optimization, which has been widely
applied to many machine learning tasks such as hyperparameter learning, meta learning …
applied to many machine learning tasks such as hyperparameter learning, meta learning …
Constrained bi-level optimization: Proximal lagrangian value function approach and hessian-free algorithm
This paper presents a new approach and algorithm for solving a class of constrained Bi-
Level Optimization (BLO) problems in which the lower-level problem involves constraints …
Level Optimization (BLO) problems in which the lower-level problem involves constraints …
Linearly constrained bilevel optimization: A smoothed implicit gradient approach
P Khanduri, I Tsaknakis, Y Zhang… - International …, 2023 - proceedings.mlr.press
This work develops analysis and algorithms for solving a class of bilevel optimization
problems where the lower-level (LL) problems have linear constraints. Most of the existing …
problems where the lower-level (LL) problems have linear constraints. Most of the existing …