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..
Fair Allocation of Items in Multiple Regions
Authors: Houyu Zhou, Tianze Wei, Biaoshuai Tao, Minming Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | On the negative side, we show NP-hardness and inapproximability results about the aforementioned fairness notions. On the positive side, we propose several algorithms to compute the partial allocations that satisfy envy-based notions and allocations that approximate the above fairness notions. |
| Researcher Affiliation | Academia | 1Department of Computer Science, City University of Hong Kong 2School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: About-To-Envy-Satisfied Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets, thus no access information for a publicly available dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not discuss any hardware used for computations or experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |