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..
Non-Convex Bilevel Games with Critical Point Selection Maps
Authors: Michael Arbel, Julien Mairal
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we show that algorithms for solving bilevel problems based on unrolled optimization solve these games up to approximation errors due to finite computational power. A simple correction to these algorithms is then proposed for removing these errors. and Numerical results illustrating the benefits of the correction are presented in Appendix E. |
| Researcher Affiliation | Academia | Michael Arbel and Julien Mairal Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France |
| Pseudocode | Yes | Algorithm 1 BGS-Opt(x0, y0) |
| Open Source Code | No | The paper does not provide explicit statements or links to open-source code for the described methodology. The checklist indicates 'N/A' for inclusion of code. |
| Open Datasets | No | The paper mentions applications in machine learning but does not provide concrete access information (link, DOI, formal citation) for any specific dataset used in its numerical results. |
| Dataset Splits | No | The paper focuses on theoretical aspects and algorithm design; it does not specify training, validation, or test dataset splits for any experiments conducted by the authors. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values or training configurations. |