On Detecting Nearly Structured Preference Profiles
Authors: Edith Elkind, Martin Lackner
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we show that these problems admit efficient approximation algorithms. Our results apply to all domains that can be characterized in terms of forbidden configurations; this includes, in particular, single-peaked and single-crossing elections. For a large range of scenarios, our approximation results are optimal under a plausible complexity-theoretic assumption. We also provide parameterized complexity results for this class of problems. |
| Researcher Affiliation | Academia | Edith Elkind University of Oxford, UK elkind@cs.ox.ac.uk Martin Lackner Vienna University of Technology, Austria lackner@dbai.tuwien.ac.at |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving specific public datasets or provide access information for any dataset used for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that would involve validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers used for experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |