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].
Eliciting Kemeny Rankings
Authors: Anne-Marie George, Christos Dimitrakakis
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | All described methods are compared on synthetic data. Finally, we analyse the sample complexity experimentally on synthetic data for all developed methods. |
| Researcher Affiliation | Academia | 1University of Oslo, Norway 2University of Neuchatel, Switzerland |
| Pseudocode | Yes | Algorithm 1: Kemeny El(Q, ρ, δ): Sampling with Replacement from Q Q(k) Algorithm 2: Kemeny El(Q, ρ, δ): Sampling w/o Replacement from Q P(k, n) Algorithm 3: Pruning CI( ˆQ, C, C) |
| Open Source Code | Yes | All code is written in Python and is publicly available under https://github.com/annemage/Eliciting-Kemeny-Rankings. |
| Open Datasets | No | The paper uses synthetically generated data. "We generate preference matrices Q P(k, n) for n = 10 voters uniformly at random for up to k = 9 arms." No concrete access information for a publicly available dataset is provided. |
| Dataset Splits | No | The paper uses synthetically generated data. It does not mention specific training, validation, or test dataset splits in terms of percentages or sample counts. The experiment focuses on sample complexity for approximation bounds. |
| Hardware Specification | No | No specific hardware details such as GPU models, CPU types, or memory amounts used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper states "All code is written in Python" but does not provide specific version numbers for Python or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | Setup: We generate preference matrices Q P(k, n) for n = 10 voters uniformly at random for up to k = 9 arms. We set an approximation value of ρ = 0.1 k(k 1) / 2 , i.e., 10% of the worst case difference in approximated and optimal Kemeny score, and approximation probability (1 δ) = 0.95. |