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..
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Authors: Shipra Agrawal, Morteza Zadimoghaddam, Vahab Mirrokni
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our ο¬rst result is that this simple, distributed algorithm converges to a (1 Ο΅)-approximate fractional b-matching solution in O( log n Ο΅2 ) rounds. We also extend the proportional allocation algorithm and convergence results to the maximum weighted matching problem... Section 4. Analysis including Proof of Theorem 1 and Proof of Theorem 2. |
| Researcher Affiliation | Collaboration | 1Columbia University, New York, NY 2Google Research. Correspondence to: Shipra Agrawal <EMAIL>, Vahab Mirrokni <EMAIL>, Morteza Zadimoghaddam <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Prop Alloc : A proportional allocation algorithm for maximum cardinality matching and Algorithm 2 Prop Alloc + : A proportional allocation algorithm for high-entropy maximum weight matching |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper does not discuss or use any datasets, as it presents a theoretical framework and algorithm analysis. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore it does not mention training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not describe any experimental setup or mention specific hardware used, as it focuses on theoretical algorithm analysis. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or their version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |