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..
Fractional Matchings under Preferences: Stability and Optimality
Authors: Jiehua Chen, Sanjukta Roy, Manuel Sorge
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We complete the complexity classification of both optimization problems for both ordinal stability and cardinal stability, distinguishing between the marriage (bipartite) and roommates (non-bipartite) cases and the presence or absence of ties in the preferences. In particular, we prove a surprising result that finding a cardinally stable fractional matching with maximum social welfare is NP-hard even for the marriage case without ties. |
| Researcher Affiliation | Academia | Jiehua Chen , Sanjukta Roy and Manuel Sorge TU Wien, Austria EMAIL |
| Pseudocode | Yes | Algorithm 1: Compute an OSM for (G, sat). ... Algorithm 2: Irving s phase-1 algo. on input (G, sat) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | This is a theoretical paper focusing on complexity and structural properties of algorithms, and does not involve the use of empirical datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical validation on datasets with training, validation, and test splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experimental setup requiring specific hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not specify software dependencies with version numbers, as it does not involve empirical implementations. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training configurations. |