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..
Private Isotonic Regression
Authors: Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
NeurIPS 2022 | Venue PDF | 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'. |