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].
Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy
Authors: Yi Liu, Qirui Hu, Linglong Kong
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through mathematical proofs and extensive numerical testing, we demonstrate that our method achieves uniform and L2 error bounds when estimating the entire CDF curve. Computationally, we demonstrate that our constrained isotonic estimator can be efficiently computed deterministically, eliminating the need for hyperparameters or random optimization. |
| Researcher Affiliation | Academia | 1Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada 2Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 Constrained isotonic estimation |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses data generated from standard statistical distributions (Uniform, Truncated normal, Continuous Bernoulli) for its experiments, rather than pre-existing public datasets with specific access information. Thus, there is no public dataset for which access information would be provided. |
| Dataset Splits | No | The paper conducts experiments by generating data from specified distributions and varying sample sizes (n spans from 10^3 to 10^7, with a total of 10,000 replications). It does not explicitly define or refer to standard train/validation/test dataset splits in percentages or counts for reproduction. |
| Hardware Specification | Yes | For n 10^7, it took less than 1s to execute on a single core of an AMD Threadripper PRO 3995WX CPU. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For the Truncated normal distribution, the parameters are set as ยต = 1/2 and ฯ2 = 1/4. In the case of the Continuous Bernoulli distribution, the parameter ฮป is selected to be 1/4... We consider the truthful response rate r = 0.25, 0.5, 0.9, which means the privacy budget is ฯต = log((1 + r)/(1 r)) corresponding to 0.51, 1.09, 2.94 respectively. The sample size ranges n spans from 10^3 to 10^7, with a total of 10,000 replications. |