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..
Fairness-Aware Estimation of Graphical Models
Authors: Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Qi Long, Li Shen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations on synthetic and real-world datasets demonstrate that our framework effectively mitigates bias without undermining GMs performance. |
| Researcher Affiliation | Academia | University of Pennsylvania {zhuopinz@sas., tarzanaq@}upenn.edu EMAIL |
| Pseudocode | Yes | Algorithm 1 Fair Estimation of GMs (Fair GMs) |
| Open Source Code | Yes | Code is available at https://github.com/Penn Shen Lab/Fair_GMs |
| Open Datasets | Yes | Experimental evaluations on synthetic and real-world datasets demonstrate that our framework effectively mitigates bias without undermining GMs performance. |
| Dataset Splits | No | The paper does not explicitly mention validation splits or specific percentages for training/validation/test. It discusses training on the entire dataset or group-specific data, and evaluating fairness metrics. |
| Hardware Specification | Yes | These experiments are conducted on an Apple M2 Pro processor. |
| Software Dependencies | No | The paper mentions software components like 'scipy.optimize.minimize' and various algorithms (e.g., 'QUIC', 'PISTA') but does not specify their version numbers, which are required for a reproducible description of software dependencies. |
| Experiment Setup | Yes | The initial iterate Ξ(0) is chosen based on the highest graph disparity error among local graphs. This initialization can improve fairness by minimizing larger disparity errors. The β1-norm coefficient Ξ» is fixed for each dataset, searched over a grid in {1e 5, . . . , 0.01, . . . , 0.1, 1}. Tolerance Ο΅ is set to 1e 5, with a maximum of 1e+7 iterations. The initial value of βis 1e 2, undergoing a line search at each iteration t with a decay rate of 0.1. |