Alternating projected sgd for equality-constrained bilevel optimization
Bilevel optimization, which captures the inherent nested structure of machine learning
problems, is gaining popularity in many recent applications. Existing works on bilevel …
problems, is gaining popularity in many recent applications. Existing works on bilevel …
Optimal algorithms for stochastic bilevel optimization under relaxed smoothness conditions
We consider stochastic bilevel optimization problems involving minimizing an upper-level
($\texttt {UL} $) function that is dependent on the arg-min of a strongly-convex lower-level …
($\texttt {UL} $) function that is dependent on the arg-min of a strongly-convex lower-level …
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 …
An alternating optimization method for bilevel problems under the Polyak-Łojasiewicz condition
Bilevel optimization has recently regained interest owing to its applications in emerging
machine learning fields such as hyperparameter optimization, meta-learning, and …
machine learning fields such as hyperparameter optimization, meta-learning, and …
[PDF][PDF] A Generalized Alternating Method for Bilevel Learning under the Polyak-{\L} ojasiewicz Condition
Bilevel optimization has recently regained interest owing to its applications in emerging
machine learning fields such as hyperparameter optimization, metalearning, and …
machine learning fields such as hyperparameter optimization, metalearning, and …
A framework for bilevel optimization on Riemannian manifolds
Bilevel optimization has seen an increasing presence in various domains of applications. In
this work, we propose a framework for solving bilevel optimization problems where variables …
this work, we propose a framework for solving bilevel optimization problems where variables …
Alternating implicit projected sgd and its efficient variants for equality-constrained bilevel optimization
Stochastic bilevel optimization, which captures the inherent nested structure of machine
learning problems, is gaining popularity in many recent applications. Existing works on …
learning problems, is gaining popularity in many recent applications. Existing works on …
An implicit gradient method for constrained bilevel problems using barrier approximation
I Tsaknakis, P Khanduri, M Hong - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
In this work, we propose algorithms for solving a class of Bilevel Optimization (BLO)
problems, with applications in areas such as signal processing, networking and machine …
problems, with applications in areas such as signal processing, networking and machine …
First-Order Methods for Linearly Constrained Bilevel Optimization
Algorithms for bilevel optimization often encounter Hessian computations, which are
prohibitive in high dimensions. While recent works offer first-order methods for …
prohibitive in high dimensions. While recent works offer first-order methods for …
Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions
Constrained bilevel optimization tackles nested structures present in constrained learning
tasks like constrained meta-learning, adversarial learning, and distributed bilevel …
tasks like constrained meta-learning, adversarial learning, and distributed bilevel …