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 Database Release via Kernel Mean Embeddings
Authors: Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | as well as basic empirical illustrations of their performance on synthetic datasets |
| Researcher Affiliation | Academia | 1MPI-IS, T ubingen, Germany 2University of Cambridge, UK. |
| Pseudocode | Yes | Algorithm 1 Differentially private database release via a synthetic data subspace of the RKHS |
| Open Source Code | Yes | Code: https://github.com/matejbalog/RKHS-private-database/ |
| Open Datasets | No | The paper uses synthetic datasets generated from a mixture of two Gaussians for its empirical illustrations, but does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses synthetic data for its experiments but does not provide specific dataset split information (percentages, counts, or detailed methodology) for training, validation, or testing its own models. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | Appendix B (Experimental Details) specifies the generation of synthetic datasets, the use of a Gaussian kernel with bandwidth σ = 1, the number of private data points N = 100,000, and for Algorithm 2, the number of random features J = 2000. |