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..
Simplification and Improvement of MMS Approximation
Authors: Hannaneh Akrami, Jugal Garg, Eklavya Sharma, Setareh Taki
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we significantly simplify the analysis of this algorithm and also improve the existence guarantee to a factor of (3/4 + 1/36, 3/4 + 1/(4n-1))). Furthermore, we present a tight example of this algorithm, showing that this may be the best factor one can hope for with the current techniques. |
| Researcher Affiliation | Collaboration | Hannaneh Akrami1,2 , Jugal Garg3 , Eklavya Sharma3 and Setareh Taki4 1Max Planck Institute for Informatics, Germany 2Graduiertenschule Informatik, Universit at des Saarlandes, Germany 3University of Illinois at Urbana-Champaign, USA 4Grubhub, USA |
| Pseudocode | Yes | Algorithm 1 normalize((N, M, v)), Algorithm 2 approx MMS(I, α), Algorithm 3 bag Fill(I, α) |
| Open Source Code | No | The paper does not provide any explicit statements or links about releasing source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper focused on algorithm analysis and approximation factors; it does not utilize datasets in an empirical context. Therefore, no information about public dataset availability is provided. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical experiments with dataset splits. No specific information about training, validation, or test splits is provided. |
| Hardware Specification | No | This is a theoretical paper focused on algorithm analysis and mathematical proofs, not empirical experiments. Therefore, no hardware specifications for running experiments are provided. |
| Software Dependencies | No | This is a theoretical paper focused on algorithm analysis and mathematical proofs, not empirical experiments. Therefore, no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | This is a theoretical paper focused on algorithm analysis and mathematical proofs, not empirical experiments. No specific experimental setup details such as hyperparameters or training configurations are provided. |