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 301–325 of 423 papers

TitleStatusHype
On the Convergence of Momentum-Based Algorithms for Federated Bilevel Optimization Problems—0
Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization—0
A Stackelberg game for incentive-based demand response in energy markets—0
A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis—0
Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization—0
Min-Max Bilevel Multi-objective Optimization with Applications in Machine LearningCode0
A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima—0
Efficiently Escaping Saddle Points in Bilevel Optimization—0
Single Time-scale Actor-critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees—0
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning—0
Bilevel Online Deep Learning in Non-stationary Environment—0
A unified algorithm framework for mean-variance optimization in discounted Markov decision processes—0
On Automatic Data Augmentation for 3D Point Cloud ClassificationCode0
A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse—0
A Novel Convergence Analysis for Algorithms of the Adam Family—0
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks—0
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems—0
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization—0
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics—0
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization—0
Amortized Implicit Differentiation for Stochastic Bilevel OptimizationCode0
BiGrad: Differentiating through Bilevel Optimization Programming—0
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)—0
Towards Evaluating the Robustness of Neural Networks Learned by TransductionCode0
PANOM: Automatic Hyper-parameter Tuning for 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