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..
Achieving Maximin Share and EFX/EF1 Guarantees Simultaneously
Authors: Hannaneh Akrami, Nidhi Rathi
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is to constructively prove the existence of (i) a partial allocation that is both 2/3-MMS and EFX, and (ii) a complete allocation that is both 2/3-MMS and EF1. Our algorithms run in pseudo-polynomial time if the approximation factor for MMS is relaxed to 2/3 ε for any constant ε > 0 and in polynomial time if, in addition, the EFX (or EF1) guarantee is relaxed to (1 δ)-EFX (or (1 δ)-EF1) for any constant δ > 0. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Informatics 2Graduiertenschule Informatik, Universit at des Saarlandes 3Saarland Informatics Campus EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: approx MMS(I), Algorithm 2: approx MMSand EFX(I), Algorithm 3: approx MMSand EF1(I) |
| Open Source Code | No | The paper does not provide any concrete access information to source code (no repository link, explicit code release statement, or code in supplementary materials). |
| Open Datasets | No | The paper discusses fair division instances in a theoretical context and does not utilize or refer to any specific publicly available datasets for experimental evaluation. |
| Dataset Splits | No | The paper focuses on theoretical algorithms and proofs, and as such, does not involve experimental evaluation with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical algorithms and provides proofs of existence and approximation factors; it does not report on experimental results that would require specific hardware. |
| Software Dependencies | No | The paper focuses on theoretical algorithm design and analysis, and therefore does not specify any software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical, presenting algorithms and proofs without empirical evaluation, and thus does not include details on experimental setup, hyperparameters, or training configurations. |