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 401–423 of 423 papers

TitleStatusHype
On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost—0
Task-Oriented Convex Bilevel Optimization with Latent Feasibility—0
Learning Surrogate Losses—0
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response FunctionsCode1
Optimal Experiment Design in Nonlinear Parameter Estimation with Exact Confidence Regions—0
MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer—0
Bilevel learning of the Group Lasso structure—0
Learning to Defend by Learning to Attack—0
Truncated Back-propagation for Bilevel OptimizationCode0
Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective—0
Deep Bilevel LearningCode0
Learning Implicit Generative Models by Teaching Explicit Ones—0
Spectral Inference Networks: Unifying Deep and Spectral LearningCode1
SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy LabelsCode0
Training Set Debugging Using Trusted Items—0
Discriminatively Learned Hierarchical Rank Pooling NetworksCode0
A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications—0
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACOCode0
OptNet: Differentiable Optimization as a Layer in Neural NetworksCode1
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners—0
A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search—0
Supervised Sparse Analysis and Synthesis Operators—0
Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization—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