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
Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach—0
Non-Convex Bilevel Optimization with Time-Varying Objective Functions—0
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis—0
DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS—0
One-step differentiation of iterative algorithms—0
On Implicit Bias in Overparameterized Bilevel Optimization—0
Online Nonconvex Bilevel Optimization with Bregman Divergences—0
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level—0
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation—0
On Stability and Generalization of Bilevel Optimization Problem—0
Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator—0
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization—0
On the Communication Complexity of Decentralized Bilevel Optimization—0
On the Complexity of First-Order Methods in Stochastic Bilevel Optimization—0
On the Convergence of Adam-Type Algorithm for Bilevel Optimization under Unbounded Smoothness—0
On the Convergence of Momentum-Based Algorithms for Federated Bilevel Optimization Problems—0
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient EstimationCode0
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDCode0
Optimizing Decomposition for Optimal Claim VerificationCode0
Few-shot Backdoor Attacks via Neural Tangent KernelsCode0
Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer DistanceCode0
Bilevel Generative Learning for Low-Light VisionCode0
Exact Certification of (Graph) Neural Networks Against Label PoisoningCode0
MAP: Towards Balanced Generalization of IID and OOD through Model-Agnostic AdaptersCode0
BILBO: BILevel Bayesian OptimizationCode0
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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