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].
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning
Authors: Niclas Boehmer, Robert Bredereck, Edith Elkind, Piotr Faliszewski, Stanisław Szufa
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use the map of elections approach of Szufa et al. (AAMAS-2020) to analyze several well-known vote distributions. For each of them, we give an explicit formula or an efficient algorithm for computing its frequency matrix, which captures the probability that a given candidate appears in a given position in a sampled vote. We use these matrices to draw the skeleton map of distributions, evaluate its robustness, and analyze its properties. Finally, we develop a general and unified framework for learning the distribution of real-world preferences using the frequency matrices of established vote distributions. |
| Researcher Affiliation | Academia | Niclas Boehmer Algorithmics and Computational Complexity Technische Universität Berlin EMAIL Robert Bredereck TU Clausthal EMAIL Edith Elkind University of Oxford EMAIL Piotr Faliszewski AGH University EMAIL Stanisław Szufa AGH University Jagiellonian University EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | The source code used for the experiments is available in a Git Hub repository1. 1github.com/Project-PRAGMA/Expected-Frequency-Matrices-Neur IPS-2022 |
| Open Datasets | Yes | We consider elections from the real-world datasets used by Boehmer et al. (2021b). They generated 15 elections with 10 candidates and 100 voters (with strict preferences) from each of the eleven different real-world election datasets (so, altogether, they generated 165 elections, most of them from Preflib (Mattei & Walsh, 2013)). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test splits for the datasets used. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or cloud resources) were mentioned for running experiments. |
| Software Dependencies | No | The paper mentions 'Python sklearn.manifold.MDS package' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | Let Φ = {0, 0.05, 0.1, . . . , 1} be a set of normalized dispersion parameters that we will be using for Mallows-based distributions in this section. |