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].
Anonymized Histograms in Intermediate Privacy Models
Authors: Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | this is a purely theoretical paper regarding private algorithms for approximately computing anonymized histograms |
| Researcher Affiliation | Industry | Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Google Research Mountain View, CA, US EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Anonymized Histogram Estimator w.r.t. 1 loss. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not describe experiments using datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe hardware specifications. In the ethics statement, it says 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]'. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details. In the ethics statement, it says 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]'. |