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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive Privacy Composition for Accuracy-first Mechanisms
Authors: Ryan M. Rogers, Gennady Samorodnitsk, Steven Z. Wu, Aaditya Ramdas
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our results to the task of releasing counts from a dataset subject to a relative error bound comparing the unified privacy filter and the baseline doubling approach, which uses the privacy filters from Whitehouse et al. [2023]. ... We perform experiments to return as many counts as possible subject to a relative error tolerance α and a privacy budget ε, δ > 0. We will generate synthetic data from a Zipf distribution ... We also evaluated our approach on real data from Reddit comments ... Our results are given in Figure 2 where we give the empirical average and standard deviation over 1000 trials for each sample size. |
| Researcher Affiliation | Collaboration | Ryan Rogers Linked In Gennady Samorodnitsky Cornell University Zhiwei Steven Wu Carnegie Mellon University Aaditya Ramdas Carnegie Mellon University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We also evaluated our approach on real data from Reddit comments from https://github.com/heyyjudes/differentially-private-set-union/tree/ ea7b39285dace35cc9e9029692802759f3e1c8e8/data. |
| Dataset Splits | No | The paper mentions varying sample sizes for the data but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, scikit-learn versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We will set a max value of the distribution to be 300 and a = 0.75. ... For the Exponential Mechanism, we select a fixed parameter εEM = 0.1. ... We also set an overall privacy budget of (ε, δ ) = (10, 10 6). ... We pick the smallest privacy parameter squared to be (ε(1) n )2 = 0.0001 for each n in both the noise reduction and the doubling method ... We then set 1000 equally spaced parameters in noise reduction ... We then vary the sample size of the data in {8000, 16000, 32000, 64000, 128000} ... using the Exponential Mechanism with εEM = 0.01, minimum privacy parameter ε(1) n = 0.0001, relative error α = 0.01, and overall (ε = 1, δ = 10 6)-DP guarantee. In 1000 trials... |