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..
Differentially Private Quantiles
Authors: Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now empirically evaluate Joint Exp against three alternatives: App Ind Exp, CSmooth, and Agg Tree. We evaluate our four algorithms on four datasets: synthetic Gaussian data from N(0, 5), synthetic uniform data from U( 5, 5), and real collections of book ratings and page counts from Goodreads (Soumik, 2019) (Figure 2). |
| Researcher Affiliation | Industry | Equal contributions, all authors at Google Research New York. Correspondence to: Jennifer Gillenwater <EMAIL>, Matthew Joseph <EMAIL>, Alex Kulesza <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Pseudocode for Joint Exp |
| Open Source Code | Yes | All experiment code is publicly available (Google, 2021). Google. dp multiq. https://github.com/google-research/google-research/tree/master/dp_multiq, 2021. |
| Open Datasets | Yes | We evaluate our four algorithms on four datasets: synthetic Gaussian data from N(0, 5), synthetic uniform data from U( 5, 5), and real collections of book ratings and page counts from Goodreads (Soumik, 2019) (Figure 2). |
| Dataset Splits | No | No specific dataset split information (percentages, counts, or predefined splits) for training, validation, or testing was found. The paper mentions '20 trials of 1000 random samples'. |
| Hardware Specification | Yes | All experiments were run on a machine with two CPU cores and 100GB RAM. |
| Software Dependencies | No | The paper mentions 'scipy.special.logsumexp' and refers to a 'racing sampling method' and numerical improvements, but does not provide specific version numbers for software dependencies like Python or SciPy itself. |
| Experiment Setup | Yes | In each case, the requested quantiles are evenly spaced. m = 1 is median estimation, m = 2 requires estimating the 33rd and 67th percentiles, and so on. We average scores across 20 trials of 1000 random samples. For every experiment, we take [ 100, 100] as the (loose) user-provided data range. For the Goodreads page numbers dataset, we also divide each value by 100 to scale the values to [ 100, 100]. Experiments for ε = 1 appear in Figure 3. |