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 [1].
Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
Authors: Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical validation includes synthetic and real-world simulations that illustrate the value and effectiveness of our proposed optimization problem and iterative algorithm.4 Experiments We illustrate the ability of Fair GLASSO to reliably estimate both synthetic and real-world graphs from data while promoting unbiased connections. |
| Researcher Affiliation | Academia | Madeline Navarro Rice University EMAIL Samuel Rey King Juan Carlos University EMAIL Andrei Buciulea King Juan Carlos University EMAIL Antonio G. Marques King Juan Carlos University EMAIL Santiago Segarra Rice University EMAIL |
| Pseudocode | Yes | Algorithm 1: Fair GLASSO from Gaussian observations. |
| Open Source Code | Yes | Moreover, the code, which is included in the submission for completeness, will be made available on Git Hub if the draft is accepted. |
| Open Datasets | Yes | The Movie Lens dataset, a common benchmark for fair graph machine learning, exemplifies our ability to form unbiased models from networks used for recommendation systems. ... (links provided in Appendix G: https://grouplens.org/, https://dl.acm.org/, http://www.sociopatterns.org/datasets/high-school-dynamic-contact-networks/, http://www.sociopatterns.org/datasets/high-school-contact-and-friendship-networks/) |
| Dataset Splits | No | Hyperparameters for optimization methods are either chosen to showcase specific scenarios as in Sections 4.2 and 4.4 or chosen via classical hyperparameter tuning methods as stated in Appendix G. |
| Hardware Specification | Yes | The experiments are run on a computer with AMD Ryzen Threadripper 3970X 32-Core Processor, two Nvidia Titan RTX GPU, and 188GB of RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Extensive experimental details including our performance metrics, the baselines with which we compare, and the real-world datasets are provided in Appendix G; these details are summarized here. We include additional experiments on the effect of varying the hyperparameters µ1 and µ2 and violating assumptions (AS1)-(AS4) of Theorem 1 on Appendix H. |