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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Computing the Yolk in Spatial Voting Games without Computing Median Lines
Authors: Joachim Gudmundsson, Sampson Wong2012-2019
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present near-linear time algorithms for computing the yolk in the plane. To the best of our knowledge our algorithm is the ο¬rst that does not precompute median lines, and hence is able to break the best known upper bound of O(n4/3) on the number of limiting median lines. We avoid this requirement by carefully applying Megiddo s parametric search technique, which is a powerful framework that could lead to faster algorithms for other spatial voting problems. |
| Researcher Affiliation | Academia | Joachim Gudmundsson, Sampson Wong University of Sydney Sydney, Australia EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms and their properties mathematically but does not include any formal pseudocode blocks or figures labeled as 'Algorithm'. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and analysis; it does not utilize datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm complexity; it does not mention any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and describes algorithms, but does not mention any specific software or programming libraries with version numbers. |
| Experiment Setup | No | The paper describes algorithms and their proofs, but does not detail an experimental setup, hyperparameters, or training configurations. |