Accountable Approval Sorting
Authors: Khaled Belahcene, Yann Chevaleyre, Christophe Labreuche, Nicolas Maudet, Vincent Mousseau, Wassila Ouerdane
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper primarily focuses on theoretical aspects such as formalizing accountability using a feasibility problem expressed as a boolean satisfiability formulation, proposing a characterization result of the sorting model, analyzing its complexity (NP-hard), and deriving a compact SAT formulation. It illustrates concepts with examples but does not report on empirical studies, dataset evaluations, performance metrics, or hypothesis validation in an experimental setup. This is evident in sections like "3 Feasibility of the Inverse NCS Problem" and its subsections. |
| Researcher Affiliation | Collaboration | The authors have mixed affiliations: '1 Laboratoire Genie Industriel, Centrale Sup elec, Universit e Paris-Saclay, Gif-sur-Yvette, France', '2 Universit e Paris-Dauphine, PSL Research University, CNRS, UMR [7243], LAMSADE, France', '3 Thales Research and Technology, Palaiseau, France', '4 Sorbonne Universit e, CNRS, Laboratoire d Informatique de Paris 6, LIP6, France'. 'Thales Research and Technology' is an industry affiliation, while the others are academic institutions, indicating a collaborative effort. |
| Pseudocode | No | The paper describes mathematical formulations, such as the boolean function φpairwise α in Section 3.4, but it does not include any clearly labeled pseudocode or algorithm blocks with structured steps in a code-like format. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicit statements about the release of code for the methodology described in the paper. |
| Open Datasets | No | The paper uses illustrative examples (e.g., Example 1, 2) to demonstrate concepts but does not utilize or refer to any publicly available or open datasets for training or evaluation. No specific access information, links, or citations for datasets are provided. |
| Dataset Splits | No | As the paper is theoretical and does not conduct experiments with datasets, it does not provide information regarding training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not describe any specific hardware used, such as GPU models, CPU types, or cloud resources, as it is a theoretical work and does not involve computational experiments that require such specifications. |
| Software Dependencies | No | The paper refers to a 'SAT solver' in Section 4.1, but it does not specify any particular SAT solver or other software dependencies with version numbers that would be needed to reproduce any computational aspects mentioned. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, such as hyperparameters, model initialization, training schedules, or specific configurations typical of empirical studies. |