Multi-Attribute Proportional Representation
Authors: Jérôme Lang, Piotr Skowron
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study some properties of the associated subset selection rules, and address their computation. In the second part of our paper we show that finding an optimal committee is often NP-hard. Yet, we show that this challenge can be addressed by designing efficient approximation and fixed-parameter tractable algorithms. |
| Researcher Affiliation | Academia | J erˆome Lang Universit e Paris-Dauphine, France; Piotr Skowron University of Oxford, United Kingdom |
| Pseudocode | Yes | Algorithm 1: Local search approximation algorithm. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | The paper uses an illustrative example with a small table of candidates (Table 1) but does not refer to any publicly available or open datasets used for training or evaluation. |
| Dataset Splits | No | The paper does not conduct empirical experiments and therefore does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and properties rather than providing details of an experimental setup, hyperparameters, or system-level training settings. |