SOTAVerified

Bilevel Optimization

Bilevel Optimization is a branch of optimization, which contains a nested optimization problem within the constraints of the outer optimization problem. The outer optimization task is usually referred as the upper level task, and the nested inner optimization task is referred as the lower level task. The lower level problem appears as a constraint, such that only an optimal solution to the lower level optimization problem is a possible feasible candidate to the upper level optimization problem.

Source: Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization

Papers

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Showing 276–300 of 423 papers

TitleStatusHype
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification—0
Adapting Triplet Importance of Implicit Feedback for Personalized RecommendationCode0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization—0
Learning Sparsity-Promoting Regularizers using Bilevel Optimization—0
Robust Network Architecture Search via Feature Distortion RestrainingCode0
Solving Bilevel Optimal Bidding Problems Using Deep Convolutional Neural Networks—0
Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient MethodsCode0
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network—0
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction—0
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural NetworksCode0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level ProblemCode0
Communication-Efficient Robust Federated Learning with Noisy Labels—0
Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective—0
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization—0
Algorithmic Foundations of Empirical X-risk Minimization—0
Will Bilevel Optimizers Benefit from Loops—0
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
Towards Extremely Fast Bilevel Optimization with Self-governed Convergence Guarantees—0
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning—0
GPN: A Joint Structural Learning Framework for Graph Neural Networks—0
Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation—0
Second-Order Sensitivity Analysis for Bilevel OptimizationCode0
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction—0
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All datasetsEquilibrium-Traffic-Networks/AnaheimEquilibrium-Traffic-Networks/Eastern MassachusettsEquilibrium-Traffic-Networks/Sioux Falls

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GIN-GAOptimality Gap0.21—Unverified
#ModelMetricClaimedVerifiedStatus
1GIN-GAOptimality Gap0.48—Unverified
#ModelMetricClaimedVerifiedStatus
1GIN-GAOptimality Gap1.44—Unverified