Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints
Authors: Liuyuan Jiang, Quan Xiao, Victor Tenorio, Fernando Real-Rojas, Antonio G. Marques, Tianyi Chen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish rigorous convergence theory for the proposed algorithm and demonstrate its effectiveness on two well-known real-world applications hyperparameter selection in support vector machine (SVM) and infrastructure planning in transportation networks using the real data from the city of Seville. |
| Researcher Affiliation | Academia | Rensselaer Polytechnic Institute, Troy, NY, United States King Juan Carlos University, Madrid, Spain |
| Pseudocode | Yes | Algorithm 1 Meta algorithm: BLOCC |
| Open Source Code | Yes | The code is available at https://github.com/Liuyuan999/Penalty Based Lagrangian Bilevel. |
| Open Datasets | Yes | We test the performance of our algorithm BLOCC with γ = 12 when training a linear SVM model on the diabetes [20] and fourclass datasets [29]. |
| Dataset Splits | Yes | The experiments are executed for 50 different random train-validation-test splits, with the bold line representing the mean, and the shaded regions being the standard deviation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or specific computing cluster configurations used for running the experiments. |
| Software Dependencies | No | The paper mentions software by name (e.g., BLOCC, LV-HBA, GAM) and sets hyperparameters for them, but does not specify version numbers for any software components, libraries, or programming languages. |
| Experiment Setup | Yes | We use γ = 12 and η = 0.01 for running our BLOCC and we apply α = 0.01, γ1 = 0.1, γ2 = 0.1, η = 0.001 for LV-HBA and α = 0.05, ϵ = 0.005 for GAM respectively. |