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..
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
Authors: Enmao Diao, Jie Ding, Vahid Tarokh
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies demonstrate that GAL can achieve performance close to centralized learning when all data, models, and objective functions are fully disclosed. |
| Researcher Affiliation | Academia | Enmao Diao Department of Electrical and Computer Engineering Duke University Durhm, NC 27705, USA EMAIL Jie Ding School of Statistics University of Minnesota-Twin Cities Minneapolis, MN 55455, USA EMAIL Vahid Tarokh Department of Electrical and Computer Engineering Duke University Durhm, NC 27705, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 GAL: Gradient Assisted Learning (from the perspective of the service receiver, Alice) |
| Open Source Code | Yes | Our code is available here 1. and 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] We provide source codes in the supplementary material. |
| Open Datasets | Yes | We demonstrate the performance of autonomous local models with UCI datasets downloadable from the scikit-learn package [24], including Diabetes [25], Boston Housing [26], Blob [24], Iris [27], Wine [28], Breast Cancer [29], and QSAR [30] datasets and Our code is available here 1. We use publicly available datasets. |
| Dataset Splits | No | For all the UCI datasets, we train on 80% of the available data and test on the remaining. |
| Hardware Specification | Yes | One Nvidia 1080TI is enough for one experiment run. |
| Software Dependencies | No | We demonstrate the performance of autonomous local models with UCI datasets downloadable from the scikit-learn package [24] |
| Experiment Setup | Yes | Details of learning hyper-parameters are included in Table 9 of the Appendix. We conducted four random experiments for all datasets with different seeds, and the standard errors are shown in the brackets of all tables. |