Proportional Fairness in Clustering: A Social Choice Perspective
Authors: Leon Kellerhals, Jannik Peters
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work does not contain any experimental results. |
| Researcher Affiliation | Academia | Leon Kellerhals Technische Universität Clausthal leon.kellerhals@tu-clausthal.de Jannik Peters National University of Singapore peters@nus.edu.sg |
| Pseudocode | No | The paper describes algorithms like GREEDY CAPTURE and SPATIAL EXPANDING APPROVALS textually but does not provide them in a structured pseudocode or algorithm block. For example: 'GREEDY CAPTURE starts off with an empty clustering W. It maintains a radius δ (initially δ = 0) and smoothly increases δ. If there is a candidate c such that at least n/k agents have distance at most δ to c, it adds c to W and deletes the n/k agents. If an agent has distance at most δ to a candidate in W, then it is deleted as well. This is continued until all agents are deleted.' |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: We provide no experimental results and use no data or code. |
| Open Datasets | No | The paper is theoretical and does not involve experimental studies with datasets. Therefore, there is no mention of training datasets or their availability. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental studies with datasets. Therefore, there is no mention of validation splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are provided. The NeurIPS Paper Checklist states: 'Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] Justification: We provide no experimental results.' |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not report on experiments. No details on experimental setup, hyperparameters, or training configurations are provided. The NeurIPS Paper Checklist states: 'Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: We provide no experimental results.' |