Multi-Criteria Dimensionality Reduction with Applications to Fairness
Authors: Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie H. Morgenstern, Santosh Vempala
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude with experiments indicating the effectiveness of algorithms based on extreme point solutions of semi-deļ¬nite programs on several real-world datasets. |
| Researcher Affiliation | Academia | Georgia Institute of Technology. {tao,ssamadi6}@gatech.edu, mohit.singh@isye.gatech.edu, jamiemmt.cs@gatech.edu, vempala@cc.gatech.edu |
| Pseudocode | Yes | The Algorithm ITERATIVE-SDP (see Figure 2 in Appendix) returns a matrix X such that |
| Open Source Code | Yes | The code is publicly available at https://github.com/SDPfor All/multi Criteria Dim Reduction. |
| Open Datasets | Yes | We perform experiments using the algorithm as outlined in Section 2 on the Default Credit data set [Yeh and Lien, 2009] |
| Dataset Splits | No | The paper mentions partitioning data into groups and preprocessing it, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU/GPU models, memory, or specific computing environments. |
| Software Dependencies | No | The paper does not specify version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | No | The paper describes the dataset used and how it's partitioned into groups, but it does not provide concrete experimental setup details such as hyperparameters (learning rates, batch sizes), optimizer settings, or other training configurations. |