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 326–350 of 423 papers

TitleStatusHype
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
A Gradient Method for Multilevel Optimization—0
Regularization Can Help Mitigate Poisoning Attacks... with the Right Hyperparameters—0
Graph Sanitation with Application to Node Classification—0
Bilevel Programs Meet Deep Learning: A Unifying View on Inference Learning Methods—0
An End-to-End Framework for Molecular Conformation Generation via Bilevel ProgrammingCode1
Randomized Stochastic Variance-Reduced Methods for Multi-Task Stochastic Bilevel Optimization—0
Implicit differentiation for fast hyperparameter selection in non-smooth convex learningCode1
Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization PerspectiveCode1
Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator—0
Generalization Guarantees for Neural Architecture Search with Train-Validation Split—0
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks—0
Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing—0
GABO: Graph Augmentations with Bi-level Optimization—0
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?Code0
Recognizing Predictive Substructures with Subgraph Information Bottleneck—0
Age of Information Optimization in a RIS-Assisted Wireless Network—0
BODAME: Bilevel Optimization for Defense Against Model Extraction—0
Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting—0
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum—0
A Single-Timescale Method for Stochastic Bilevel Optimization—0
Lower Bounds and Accelerated Algorithms for Bilevel Optimization—0
Load Encoding for Learning AC-OPF—0
Integrated Optimization of Predictive and Prescriptive Tasks—0
Combining Physics and Machine Learning for Network Flow Estimation—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