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..
Provably Faster Algorithms for Bilevel Optimization
Authors: Junjie Yang, Kaiyi Ji, Yingbin Liang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments validate our theoretical results and demonstrate the superior empirical performance of our algorithms in hyperparameter applications. |
| Researcher Affiliation | Academia | Junjie Yang Department of ECE The Ohio State University EMAIL Kaiyi Ji Department of EECS University of Michigan EMAIL Yingbin Liang Department of ECE The Ohio State University EMAIL |
| Pseudocode | Yes | Algorithm 1 Momentum-based Recursive Bilevel Optimizer (MRBO) Algorithm 2 Variance Reduction Bilevel Optimizer (VRBO) |
| Open Source Code | Yes | Our codes are available online at https://github.com/JunjieYang97/MRVRBO. Our code is public on Git Hub. |
| Open Datasets | Yes | Our experiments are run over a hyper-cleaning application on MNIST. We specify that the dataset we use are public in Appendix B. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] The experimental details are specified in Appendix B. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] The details are in included in Appendix B. |
| Software Dependencies | No | The paper does not explicitly list software dependencies with specific version numbers in the main text or refer to such details being in the appendix. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] The experimental details are specified in Appendix B. |