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..
Multi-Attribute Proportional Representation
Authors: Jérôme Lang, Piotr Skowron
AAAI 2016 | Venue PDF | 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. |