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 Pareto Optimal Committees
Authors: Haris Aziz, Jérôme Lang, Jérôme Monnot
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For each of the notions, we undertake a detailed study of complexity of computing and verifying Pareto optimal outcomes. Table 1 summarizes the complexity results. |
| Researcher Affiliation | Collaboration | Haris Aziz NICTA and UNSW Sydney, Australia EMAIL J erˆome Lang and J erˆome Monnot LAMSADE, Universit e Paris-Dauphine Paris, France EMAIL |
| Pseudocode | Yes | Algorithm 1: Committee Voting Serial Dictatorship |
| 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 involve experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that would require dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |