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 [1].
Multiwinner Elections under Minimax Chamberlin-Courant Rule in Euclidean Space
Authors: Chinmay Sonar, Subhash Suri, Jie Xue
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that the problem is NPhard in any dimension d 2, and also provably hard to approximate. Our main results are three polynomial-time approximation schemes, each of which finds a committee with provably good minimax score. |
| Researcher Affiliation | Academia | 1University of California, Santa Barbara 2New York University, Shanghai EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms verbally (e.g., "Our algorithm begins with an empty committee T = and iteratively adds new candidates to T using the following three steps until S[T] = V ") but does not present them in a structured pseudocode block. |
| Open Source Code | No | The paper does not provide any statements about open-sourcing code or links to repositories. |
| Open Datasets | No | The paper is theoretical and focuses on computational complexity and approximation algorithms in Euclidean space. It does not use specific datasets for training or empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on proofs and algorithm design. It does not describe any specific software dependencies or version numbers, as no empirical implementation or experiments are detailed. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations, as it does not involve empirical experiments. |