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..
Heavy Ball Momentum for Conditional Gradient
Authors: Bingcong Li, Alireza Sadeghi, Georgios Giannakis
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results demonstrate the usefulness of heavy ball momentum in FW iterations. |
| Researcher Affiliation | Academia | University of Minnesota Twin Cities Minneapolis, MN, USA |
| Pseudocode | Yes | Algorithm 1 FW [9] ... Algorithm 2 FW with heavy ball momentum ... Algorithm 3 FW with heavy ball momentum and restart |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | Yes | Datasets from LIBSVM2 are used in the numerical tests, where details of the datasets are deferred to Appendix F due to space limitation. 2https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages or sample counts for training, validation, and test sets). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the algorithms and their theoretical properties but does not provide specific experimental setup details like hyperparameter values, learning rates, or batch sizes. |