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..
Differentially Private Fair Division
Authors: Pasin Manurangsi, Warut Suksompong
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present algorithms for approximate envy-freeness and proportionality... On the other hand, we provide strong negative results...Our goal is to devise ε-DP algorithms...or to prove that this task is impossible. |
| Researcher Affiliation | Collaboration | Pasin Manurangsi1, Warut Suksompong2 1Google Research 2National University of Singapore |
| Pseudocode | Yes | Algorithm 1: (Agent item)-level ε-DP algorithm for EFc |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology or any links to a code repository. |
| Open Datasets | No | This paper is theoretical and focuses on algorithms and proofs for fair division under differential privacy, not on empirical evaluations using publicly available datasets for training. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and proofs; it does not mention any software dependencies with specific version numbers needed for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiment setups, hyperparameters, or training configurations. |