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].
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. |