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Found 13 papers, 5 papers with code
Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm
To address this challenge, we begin by devising a smooth proximal Lagrangian value function to handle the constrained lower-level problem.
Moreau Envelope Based Difference-of-weakly-Convex Reformulation and Algorithm for Bilevel Programs
In a recent study by Ye et al. (2023), a value function-based difference of convex algorithm was introduced to address bilevel programs.
Hierarchical Optimization-Derived Learning
In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been proposed to address diverse learning and vision tasks.
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.
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization.
Value Function Based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems
Gradient-based optimization methods for hyperparameter tuning guarantee theoretical convergence to stationary solutions when for fixed upper-level variable values, the lower level of the bilevel program is strongly convex (LLSC) and smooth (LLS).
Towards Extremely Fast Bilevel Optimization with Self-governed Convergence Guarantees
Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning and vision fields.
Value-Function-based Sequential Minimization for Bi-level Optimization
We also extend BVFSM to address BLO with additional functional constraints.
Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond
In particular, by introducing an auxiliary as initialization to guide the optimization dynamics and designing a pessimistic trajectory truncation operation, we construct a reliable approximate version of the original BLO in the absence of LLC hypothesis.
A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization
Bi-level optimization model is able to capture a wide range of complex learning tasks with practical interest.
A General Descent Aggregation Framework for Gradient-based Bi-level Optimization
In this work, we formulate BLOs from an optimistic bi-level viewpoint and establish a new gradient-based algorithmic framework, named Bi-level Descent Aggregation (BDA), to partially address the above issues.
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
In recent years, a variety of gradient-based first-order methods have been developed to solve bi-level optimization problems for learning applications.
Task-Oriented Convex Bilevel Optimization with Latent Feasibility
This paper firstly proposes a convex bilevel optimization paradigm to formulate and optimize popular learning and vision problems in real-world scenarios.
