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..
Matchings under One-Sided Preferences with Soft Quotas
Authors: Santhini K. A., Raghu Raman Ravi, Meghana Nasre
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present efficient algorithms based on flow-networks to solve these optimization problems. Theorem 1. OPT-SIGN-MIN-MAX and OPT-SIGN-MIN-TOT admit polynomial time algorithms, where OPT can be one of rank-maximality (RMM) or fairness (FAIR). Theorem 2. OPT-MIN-MAX and OPT-MIN-TOT admit polynomial time algorithms, where OPT can be one of rank-maximality or fairness. |
| Researcher Affiliation | Academia | Santhini K. A.1 , Raghu Raman Ravi2 and Meghana Nasre1 1Indian Institute of Technology Madras 2ETH Zurich EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms textually and uses flow network diagrams, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or evaluation, therefore no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no information about training, validation, or test splits is provided. |
| Hardware Specification | No | The paper describes theoretical algorithms and does not report on empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes theoretical algorithms and does not report on empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and describes algorithm design and proofs, not empirical experiments. Therefore, no experimental setup details like hyperparameters or training configurations are provided. |