Byzantine Spectral Ranking

Authors: Arnhav Datar, Arun Rajkumar, John Augustine

NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In this section, we confirm our theoretical bounds with experiments conducted on synthetic and real data.
Researcher Affiliation Academia Arnhav Datar Indian Institute of Technology, Madras adatar@cmu.edu Arun Rajkumar Indian Institute of Technology, Madras arunr@cse.iitm.ac.in John Augustine Indian Institute of Technology, Madras augustine@iitm.ac.in
Pseudocode Yes Algorithm 1 Rank-Centrality Algorithm 2 Byzantine Spectral Ranking Algorithm 3 Fast Byzantine Spectral Ranking
Open Source Code No The paper states 'Yes' in the checklist for including code, data, and instructions needed to reproduce main experimental results, but does not provide a direct link or explicit statement about open-sourcing its *own* code within the main body of the paper.
Open Datasets Yes We consider the Sushi dataset comprising 5000 voters ordering the 10 sushis from most preferred to least preferred.
Dataset Splits No The paper does not explicitly provide specific train/validation/test dataset splits, percentages, or methods for partitioning the data, for either the synthetic or real datasets.
Hardware Specification No The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running its experiments.
Software Dependencies No The paper does not provide a reproducible description of ancillary software with specific version numbers for key components or libraries.
Experiment Setup Yes We consider the following parameters in our testing (1) n = 200 (2) k = 100 (3) p = 20 log n/n and (4) wi = Uniform(1, 100). We applied the FBSR Algorithm with the following modifications considering the smaller k and n values: (1) setting 5δ = 1 + psize(Bbucket) and (2) setting max_out as k/20.