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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
Bayesian Optimization of Bilevel Problems—0
Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation—0
BiAdam: Fast Adaptive Bilevel Optimization Methods—0
BiGrad: Differentiating through Bilevel Optimization Programming—0
Bilevel Continual Learning—0
Task-Oriented Convex Bilevel Optimization with Latent Feasibility—0
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)—0
Bilevel learning of the Group Lasso structure—0
Bilevel Model for Electricity Market Mechanism Optimisation via Quantum Computing Enhanced Reinforcement Learning—0
Bilevel Online Deep Learning in Non-stationary Environment—0
Bilevel Optimization, Deep Learning and Fractional Laplacian Regularization with Applications in Tomography—0
Bilevel Optimization for Differentially Private Optimization in Energy Systems—0
Bilevel Optimization for Feature Selection in the Data-Driven Newsvendor Problem—0
Bilevel Optimization for Machine Learning: Algorithm Design and Convergence Analysis—0
Bilevel Optimization for Real-Time Control with Application to Locomotion Gait Generation—0
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction—0
Bi-Level Policy Optimization with Nyström Hypergradients—0
Bilevel Programs Meet Deep Learning: A Unifying View on Inference Learning Methods—0
Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity—0
Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques for Efficient LLM Fine-Tuning and Meta-Training—0
BiSSL: Enhancing the Alignment Between Self-Supervised Pretraining and Downstream Fine-Tuning via Bilevel Optimization—0
BODAME: Bilevel Optimization for Defense Against Model Extraction—0
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems—0
Combining Physics and Machine Learning for Network Flow Estimation—0
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation—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