Differentially Private Fair Division
Authors: Pasin Manurangsi, Warut Suksompong
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |