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
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization—0
Hyperparameter Tuning Through Pessimistic Bilevel Optimization—0
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy—0
Implicit Bilevel Optimization: Differentiating through Bilevel Optimization Programming—0
Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso—0
Implicit Diffusion: Efficient Optimization through Stochastic Sampling—0
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence—0
Integrated Optimization of Predictive and Prescriptive Tasks—0
Integrating process design and control using reinforcement learning—0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systems—0
Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems—0
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems—0
Joint Design of Conventional Public Transport Network and Mobility on Demand—0
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning—0
Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios—0
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization—0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations—0
Learning Implicit Generative Models by Teaching Explicit Ones—0
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks—0
Learning Sample Reweighting for Accuracy and Adversarial Robustness—0
Learning Sample Reweighting for Adversarial Robustness—0
Learning Sparsity-Promoting Regularizers using Bilevel Optimization—0
Learning sparsity-promoting regularizers for linear inverse problems—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