Almost Envy-Freeness for Groups: Improved Bounds via Discrepancy Theory
Authors: Pasin Manurangsi, Warut Suksompong
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our proofs make extensive use of tools from discrepancy theory.Our main tools and techniques throughout this work come from discrepancy theory, an area of mathematics that studies how much deviation from the desired state is necessary in various settings we provide the relevant background in Section 2.2.In this section, we derive generic upper and lower bounds for the value c CD k based on the multi-color discrepancy bounds discmax(n, k). Our results are stated formally below. |
| Researcher Affiliation | Collaboration | 1Google Research, USA 2School of Computing, National University of Singapore, Singapore |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper describes theoretical research and does not involve training models on datasets, therefore no concrete access information for a publicly available or open dataset is provided. |
| Dataset Splits | No | The paper describes theoretical research and does not provide specific dataset split information as it does not conduct experiments with data. |
| Hardware Specification | No | The paper describes theoretical research and does not provide specific hardware details for running experiments. |
| Software Dependencies | No | The paper describes theoretical research and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper describes theoretical research and does not provide specific experimental setup details as it does not conduct experiments. |