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 [1].
Differentially private exact recovery for stochastic block models
Authors: Dung Nguyen, Anil Kumar Vullikanti
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our focus here is on the theoretical foundations of the problem. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA. |
| Pseudocode | Yes | Algorithm 1 Mf Stbl(G): Stability Mechanism |
| Open Source Code | No | We maintain a full, updated version of this work at (Nguyen & Vullikanti, 2024). (URL https://arxiv.org/abs/2406.02644, which points to the paper itself, not source code.) |
| Open Datasets | No | The paper is theoretical and focuses on derivations and conditions; it does not use or reference specific datasets for training or empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and focuses on derivations and conditions; it does not use or reference specific datasets for validation or empirical evaluation, nor does it discuss dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical derivations and algorithm design; it does not describe computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations and algorithm design; it does not list specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical derivations and algorithm design; it does not describe an empirical experimental setup with hyperparameters or training configurations. |