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].
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. |