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..
Differential Privacy on Fully Dynamic Streams
Authors: Yuan Qiu, Ke Yi
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theoretical paper that does not include experiments. |
| Researcher Affiliation | Academia | Yuan Qiu School of Cyber Science and Engineering Southeast University Nanjing, China 211189 EMAIL Ke Yi Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong SAR, China 999077 EMAIL |
| Pseudocode | Yes | Algorithm 1: (ε, δ)-DP Algorithm at node v = vi |
| Open Source Code | No | This is a theoretical paper that does not include experiments. The paper does not mention any release of source code or provide links to a repository. |
| Open Datasets | No | This is a theoretical paper that does not include experiments. The paper does not mention using or providing access to any specific datasets for empirical validation. |
| Dataset Splits | No | This is a theoretical paper that does not include experiments. Therefore, there is no mention of dataset splits. |
| Hardware Specification | No | This is a theoretical paper that does not include experiments. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper that does not include experiments. Therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This is a theoretical paper that does not include experiments. Therefore, no experimental setup details like hyperparameters or training configurations are provided. |