Fair Rank Aggregation
Authors: Diptarka Chakraborty, Syamantak Das, Arindam Khan, Aditya Subramanian
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our first main contribution is an exact algorithm for the closest fair ranking (CFR) problem under proportional fairness (see Definition 2.4) for Kendall tau and Ulam metrics. ...Our algorithms are simple, fast, and might be extendable to other relevant metrics. We also give a novel meta-algorithm for the general rank aggregation problem under the fairness framework. ...Furthermore, using sophisticated techniques we obtain a (3 ε)-approximation algorithm, for a constant ε > 0, for the Ulam metric under strong fairness. |
| Researcher Affiliation | Academia | Diptarka Chakraborty School of Computing National University of Singapore diptarka@comp.nus.edu.sg Syamantak Das Department of Computer Science and Engineering Indraprastha Institute of Information Technology, Delhi syamantak@iiit.ac.in Arindam Khan Department of Computer Science and Automation Indian Institute of Science, Bengaluru arindamkhan@iisc.ac.in Aditya Subramanian Department of Computer Science and Automation Indian Institute of Science, Bengaluru adityasubram@iisc.ac.in |
| Pseudocode | Yes | See Algorithm 1 in the appendix for the pseudocode of the algorithm. ...See Algorithm 2 in the appendix for a formal description of the algorithm. |
| Open Source Code | No | The paper focuses on theoretical contributions (algorithms, proofs, approximation ratios) and does not mention or provide any open-source code for its methodology. The ethics statement also does not indicate code release. |
| Open Datasets | No | This paper is purely theoretical and does not involve experimental evaluation on datasets. Therefore, it does not discuss public datasets or their availability for training. |
| Dataset Splits | No | This paper is purely theoretical and does not involve experimental evaluation on datasets, so there are no discussions of training, validation, or test splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup or the hardware used to conduct experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and analysis. It does not mention any specific software dependencies or their version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and presents algorithms and proofs. It does not describe an experimental setup, hyperparameters, or system-level training settings. |