Non-Convex Bilevel Games with Critical Point Selection Maps
Authors: Michael Arbel, Julien Mairal
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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. |