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..
Stable Matching with Ties: Approximation Ratios and Learning
Authors: Shiyun Lin, Simon Mauras, Nadav Merlis, Vianney Perchet
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper does not include experiments. |
| Researcher Affiliation | Collaboration | Shiyun Lin Center for Statistical Science School of Mathematical Sciences, Peking University EMAIL Simon Mauras INRIA, Fair Play Joint Team EMAIL Nadav Merlis Technion Israel Institute of Technology EMAIL Vianney Perchet CREST, ENSAE, IP Paris Criteo AI Lab, Fair Play Joint Team EMAIL |
| Pseudocode | Yes | Algorithm 1 Internally Stable Matchings for Matching Market with Indifference, Algorithm 2 ϵ-Oracle for Approximated Worker Optimal Stable Matching, Algorithm 3 Explore-then-Choose-Oracle (Full version) |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments. No data or models are released with this paper. |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |