Provably Faster Algorithms for Bilevel Optimization
Authors: Junjie Yang, Kaiyi Ji, Yingbin Liang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 yang.4972@osu.edu Kaiyi Ji Department of EECS University of Michigan kaiyiji@umich.edu Yingbin Liang Department of ECE The Ohio State University liang.889@osu.edu |
| 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. |