Private Isotonic Regression
Authors: Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Since this is a purely theoretical paper regarding private algorithms for well studied ML task of isotonic regression, we do not foresee any immediate potential negative impacts. |
| Researcher Affiliation | Industry | Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Google Research Mountain View, CA, US |
| Pseudocode | Yes | Algorithm 1 DP Isotonic Regression for Totally Ordered Sets. |
| Open Source Code | No | The paper states 'N/A' for code in the 'If you ran experiments...' section, indicating no open-source code is provided. |
| Open Datasets | No | This is a theoretical paper and does not describe the use of any specific, publicly available dataset for training or evaluation. The 'If you ran experiments...' section is marked 'N/A'. |
| Dataset Splits | No | This is a theoretical paper and does not describe empirical experiments with dataset splits. The 'If you ran experiments...' section is marked 'N/A'. |
| Hardware Specification | No | This is a theoretical paper and does not mention specific hardware used for experiments. The 'If you ran experiments...' section is marked 'N/A'. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers for experimental reproducibility. The 'If you ran experiments...' section is marked 'N/A'. |
| Experiment Setup | No | This is a theoretical paper and does not provide details about an experimental setup, hyperparameters, or training settings. The 'If you ran experiments...' section is marked 'N/A'. |