Achieving Proportional Representation in Conference Programs
Authors: Ioannis Caragiannis, Laurent Gourvès, Jérôme Monnot
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that different variations of the problem are computationally hard by exploiting relations of the problem with well-known hard graph problems. On the positive side, we present polynomial-time algorithms that compute conference programs that have a social utility that is provably close to the optimal one (within constant factors). Our algorithms are either combinatorial or based on linear programming and randomized rounding. |
| Researcher Affiliation | Academia | Ioannis Caragiannis University of Patras & CTI Diophantus , Greece; Laurent Gourv es CNRS, Universit e Paris-Dauphine, France; J erˆome Monnot CNRS, Universit e Paris-Dauphine, France |
| Pseudocode | No | The paper describes algorithms in prose (e.g., 'Our next algorithm is based on linear programming and randomized rounding'), but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets, therefore, there is no mention of training data availability. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset usage, thus no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe empirical experiments, and therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and discusses algorithms (e.g., Edmonds' algorithm, linear programming) without specifying any particular software dependencies with version numbers for implementation or experimental setup. |
| Experiment Setup | No | The paper focuses on theoretical analysis and algorithm design. It does not provide details on practical experimental setups, such as hyperparameters or training configurations. |