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..
Improved Maximin Share Approximations for Chores by Bin Packing
Authors: Jugal Garg, Xin Huang, Erel Segal-Halevi
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show the existence of 1-out-of-9/11n MMS allocations, which improves the state-of-the-art factor of 1-out-of-3/4n. MMS allocations for factored instances, which resolves an open question posed by Ebadian et al. (2021). 15/13-MMS allocations for personalized bivalued instances, improving the state-of-the-art factor of 13/11. We achieve these results by leveraging the HFFD algorithm of Huang and Lu (2021). Our approach also provides polynomial-time algorithms for computing an MMS allocation for factored instances and a 15/13-MMS allocation for personalized bivalued instances. |
| Researcher Affiliation | Academia | Jugal Garg1, Xin Huang2, Erel Segal-Halevi3 1University of Illinois at Urbana Champaign, USA 2Kyushu University, Fukuoka, Japan 3Ariel University, Ariel 40700, Israel EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Reduction by swapping for a factored cost function... Algorithm 2: Reduction by swapping for a bivalued cost function |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicit statements about code availability for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical analysis of chore allocation algorithms and does not use or refer to any specific publicly available datasets for experimental evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation on datasets, thus no dataset splits are mentioned. |
| Hardware Specification | No | The paper focuses on theoretical proofs and algorithm design, not experimental evaluation, and therefore does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithmic design and analysis, without detailing any software dependencies or versions for implementation or experimentation. |
| Experiment Setup | No | The paper presents theoretical results and algorithms rather than empirical experiments, so it does not contain details about experimental setup or hyperparameters. |