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..
Approximately EFX Allocations for Indivisible Chores
Authors: Shengwei Zhou, Xiaowei Wu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose polynomial-time algorithms for the computation of approximately EFX allocations for indivisible chores. For three agents, our algorithm achieves an approximation ratio of 5 while for n 4 agents the approximation ratio is 3n2. Prior to our work, no non-trivial results regarding the approximation of EFX allocation for chores are known, except for some special cases [Li et al., 2022]. |
| Researcher Affiliation | Academia | Shengwei Zhou , Xiaowei Wu IOTSC, University of Macau EMAIL, |
| Pseudocode | Yes | Algorithm 1: Sequential Placement; Algorithm 2: Algorithm for 2 Large Agents; Algorithm 3: Algorithm for 3 Large Agents |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets. Therefore, no information about public datasets or their access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data. Thus, there is no discussion of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameter values, or system-level training settings as it does not conduct empirical experiments. |