Optimal Private Median Estimation under Minimal Distributional Assumptions
Authors: Christos Tzamos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Ilias Zadik
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present simulation results on synthetic and real data to validate the performance of our proposed Private Median Estimator (PME). We compare our PME with various baselines, including Private Mean Estimator (PME) based on sample mean and the Private Median Estimator (PME) based on sample median. |
| Researcher Affiliation | Academia | School of Computer Science, University of Massachusetts Amherst, MA, USA Electrical and Computer Engineering, University of Texas at Austin, TX, USA Department of Computer Science and Engineering, University of California, San Diego, CA, USA |
| Pseudocode | Yes | Algorithm 1: Private Median Estimation (Page 4), Algorithm 2: Private Quantile Estimation (Page 5) |
| Open Source Code | No | No explicit statement or link providing access to the open-source code for the described methodology was found. |
| Open Datasets | Yes | For real-world datasets, we use the Adult dataset [16] and the Bank Marketing dataset [17] from the UCI Machine Learning Repository. [16] Dua, D. and Graff, C. UCI machine learning repository, 2017. [17] S. Moro, P. Cortez, and P. Rita. A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62:22–31, June 2014. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing was found. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, or detailed computer specifications) used for running experiments were found. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) were found. |
| Experiment Setup | Yes | For synthetic data experiments, we vary the number of samples n from {1000, 5000, 10000} and the privacy budget ε from {0.1, 0.5, 1.0}. We conduct 100 independent runs for each setting and report the average estimation error. |