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 Fractional Frequency Moments Estimation with Polylogarithmic Space
Authors: Lun Wang, Iosif Pinelis, Dawn Song
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation shows that Fp sketch can achieve reasonable accuracy with differential privacy guarantee. The evaluation code is included in the supplementary material. 4 EVALUATION |
| Researcher Affiliation | Academia | Lun Wang UC Berkeley EMAIL Iosif Pinelis Michigan Tech EMAIL Dawn Song UC Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1: Fp sketch. Algorithm 2: Fp sketch with sub-sampling. |
| Open Source Code | Yes | The evaluation code is included in the supplementary material. |
| Open Datasets | Yes | Real-world Data. We also evaluate Fp sketches using real-world application usage data (Ye et al., 2019) collected by Talking Data SDK. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, only mentions data sampling and multiple runs for statistical analysis. |
| Hardware Specification | Yes | All the experiments were run on a Ubuntu18.04 LTS server with 32 AMD Opteron(TM) Processor 6212 with 512GB RAM. |
| Software Dependencies | No | The paper mentions 'Ubuntu18.04 LTS server' but does not specify other software dependencies with version numbers. |
| Experiment Setup | Yes | For all the evaluation, the sketch size r is 50 as suggested in Li (2008). The sub-sampling rate in all the experiments is 0.02. |