Online Local Differential Private Quantile Inference via Self-normalization
Authors: Yi Liu, Qirui Hu, Lei Ding, Linglong Kong
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With mathematical proof and extensive numerical testing, we demonstrate the validity of our algorithm both theoretically and experimentally. and Finally, we provide experimental results to demonstrate the effectiveness of our approach. |
| 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 Locally Randomized Compare (LRC), Algorithm 2 Main Algorithm, Algorithm 3 Generate Confidence Interval |
| Open Source Code | No | The paper does not provide information about open-source code for the described methodology. |
| Open Datasets | Yes | The data come from four cases: standard Normal N(0, 1), Uniform U( 1, 1), standard Cauchy C(0, 1), and PERT distribution (Clark, 1962) with probability density function: f(x) = 0.625(1 x)(1 + x)3, x ( 1, 1). |
| Dataset Splits | No | The paper describes simulation experiments using generated i.i.d. samples from specified distributions and does not mention explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use the step sizes dn = 2/(n0.51+100) for all experiments, which satisfies the assumptions of Theorem 3.3 and 3.4. The range of sample size n is (10000, 400000), the initial value q0 = 0, and the number of replication is 10000. |