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].
Local Differential Privacy Meets Computational Social Choice - Resilience under Voter Deletion
Authors: Liangde Tao, Lin Chen, Lei Xu, Weidong Shi
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate Po LDP through numeric experiments. In our experiments, the number of voters is set to be n = 108. The number of candidates is set to be m = 2; 5. In this experiment, we generate the true type of each voter using two methods. |
| Researcher Affiliation | Academia | Liangde Tao1 , Lin Chen2 , Lei Xu3 and Weidong Shi4 1Zhejiang University 2Texas Tech University 3Kent State University 4University of Houston |
| Pseudocode | No | The paper presents mathematical formulations like Integer Linear Programs (ILP) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/polyapp/poldp |
| Open Datasets | Yes | The second method randomly generates the true type of each voter according to the real-world data Sushi Data [Kamishima, 2003] which is a commonly used data set for generating preferences [Azari et al., 2012]. |
| Dataset Splits | No | The paper describes how data is generated for experiments but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments are performed on a computer with an Intel i7-6700 processor and 32GB memory. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The parameter is set to be ΞΎ = 0.999, Ξ΄ = 0.001, Ο΅ = 0.001. |