Fast Private Kernel Density Estimation via Locality Sensitive Quantization
Authors: Tal Wagner, Yonatan Naamad, Nina Mishra
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that our resulting DP-KDE mechanisms are fast and accurate on large datasets in both high and low dimensions. |
| Researcher Affiliation | Industry | 1Amazon. Correspondence to: Tal Wagner <tal.wagner@gmail.com>. |
| Pseudocode | Yes | Algorithm 1: LSQ Mechanism for DP-KDE Curator |
| Open Source Code | Yes | Our code is available online.2 (2https://github.com/talwagner/lsq) |
| Open Datasets | Yes | Covertype: forest cover types (n = 581,012, d = 55) (Blackard & Dean, 1999) Glo Ve: word embeddings (n = 1,000,000, d = 100) (Pennington et al., 2014) Diabetes: age and days in hospital (n = 101,766, d = 2) (Strack et al., 2014) NYC Taxi: longitude and latitude (n = 100,000, d = 2) (Chavez et al., 2018) |
| Dataset Splits | No | The paper mentions holding out query points but does not specify explicit training/validation/test splits (e.g., percentages, counts, or cross-validation). |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU/CPU models, memory, or cloud instances) used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | In LSQ-RFF, we parameterize the mechanism by the number of random Fourier features... In LSQ-FGT, the user selects an integer parameter ρ ≥ 1... For each dataset we tune the bandwidth according to the guidelines in prior work... Bandwidth values are tuned are such that mean KDE values are on the order of 10^-2 and their standard deviation is also on the order of 10^-2 |