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].
Equal Opportunity of Coverage in Fair Regression
Authors: Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, Philip S Yu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the effectiveness of our method in improving EOC. |
| Researcher Affiliation | Collaboration | Fangxin Wang University of Illinois Chicago Chicago, USA EMAIL Lu Cheng University of Illinois Chicago Chicago, USA EMAIL Ruocheng Guo Byte Dance Research London, UK EMAIL Kay Liu University of Illinois Chicago Chicago, USA EMAIL Philip S. Yu University of Illinois Chicago Chicago, USA EMAIL |
| Pseudocode | Yes | To address this issue, we propose Binned Fair Quantile Regression (BFQR) (see Algorithm 1 in Appendix 8.2.1). |
| Open Source Code | Yes | Our code is publicly available at https://github.com/fangxin-wang/bfqr. |
| Open Datasets | Yes | We further evaluate our method on two benchmark datasets: Adult [44, 45] where gender is the protected attribute and the outcome is salary; MEPS (Medical Expenditure Panel Survey) data [46, 11] where race is the protected attribute and the outcome is the health care system utilization score. |
| Dataset Splits | Yes | Every experiment is repeated 100 times on random divisions of data with different seeds, with |Dtr| : |Dc| : |Dt| = 3 : 1 : 1. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as CPU or GPU models, or cloud computing instances with detailed specifications. |
| Software Dependencies | No | The paper mentions using a 'QR model' as the base for conformal prediction methods but does not provide specific software names with version numbers for reproducibility (e.g., 'PyTorch 1.9', 'scikit-learn 1.0'). |
| Experiment Setup | Yes | The base model for all compared conformal prediction methods is set as the QR model at the level of 0.05 and 0.95, and the desired marginal coverage is set to 0.9. |