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..
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
Authors: Bo Liu, Mao Ye, Stephen Wright, Peter Stone, Qiang Liu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide a non-asymptotic convergence analysis of the proposed method to stationary points for non-convex objectives and present empirical results that show its superior practical performance. |
| Researcher Affiliation | Collaboration | Bo Liu1 Mao Ye1 Stephen Wright2 Peter Stone1,3 Qiang Liu1 1The University of Texas at Austin 2University of Wisconsin-Madison 3 Sony AI |
| Pseudocode | Yes | Algorithm 1 Bilevel Optimization Made Easy (BOME!) |
| Open Source Code | Yes | 3. (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | For the dataset, we use MNIST [9] (Fashion MNIST [50]). |
| Dataset Splits | Yes | The stepsizes of all methods are set by a grid search from the set {0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000}. All toy problems adopt vanilla gradient descent (GD) and applications on hyperparameter optimization adapts GD with a momentum of 0.9. |
| Hardware Specification | No | The paper states in its checklist: "3. (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No]". No specific hardware details are mentioned in the main text. |
| Software Dependencies | No | The paper mentions using "Adam [26]" as an optimizer, but does not provide specific version numbers for any software dependencies, programming languages, or libraries like PyTorch, TensorFlow, Python, or CUDA. |
| Experiment Setup | Yes | Unless otherwise specified, BOME strictly follows Algorithm 1 with φk = krˆq(vk, k)k2, = 0.5, and T = 10. The inner stepsize is set to be the same as outer stepsize . The stepsizes of all methods are set by a grid search from the set {0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000}. All toy problems adopt vanilla gradient descent (GD) and applications on hyperparameter optimization adapts GD with a momentum of 0.9. |