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 Source-Target Clustering
Authors: Shachar Schnapp, Sivan Sabato
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate in experiments the reduction in clustering cost that is obtained by our practical algorithms compared to baseline approaches. Code is publicly available on https://github.com/Shachar Schnapp/STC. [...] We ran our algorithms on synthetic and real-world datasets, and compared them to the following ε-DP baselines: |
| Researcher Affiliation | Academia | Shachar Schnapp EMAIL Department of Computer Science, Ben-Gurion University of the Negev Sivan Sabato EMAIL Department of Computing and Software, Mc Master University Canada CIFAR AI Chair, Vector Institute Department of Computer Science, Ben-Gurion University of the Negev |
| Pseudocode | Yes | Algorithm 1 Noisy Average Set (NAS) [...] Algorithm 2 Neighbor Noisy Averages (NNA) |
| Open Source Code | Yes | Code is publicly available on https://github.com/Shachar Schnapp/STC. [...] The python code is publicly available on https://github.com/Shachar Schnapp/STC. |
| Open Datasets | Yes | We ran our algorithms on synthetic and real-world datasets [...] MNIST (Deng, 2012) contains 70,000 grayscale images of handwritten digits. [...] Office (Saenko et al., 2010) contains images of office items from different sources: [...] Superconductivity (Hamidieh, 2018) is an 82-dimensional dataset of 16,000 superconducting materials. |
| Dataset Splits | Yes | We tested three (source,target) pairs of digits: (1,7), (5,2) and (9, 6). [...] We split the dataset into four subsets termed low (l), middle-low (ml), middle-high (mh) and high (h) of around 4000 instances each. We tested all possible (source, target) pairs. |
| Hardware Specification | Yes | All run times were measured when running on one core of an Intel i9-9900K CPU and NVIDIA GEFORCE RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions 'python code' and refers to specific algorithms like 'Accelerated K-medoids (Ak M)' but does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | For Alg. 1, we tested several values of t and got similar results, thus we provide below results for t = 150, and report the others in Appendix C.1. [...] We fixed ε = 3 for DP and ρ = 3 for z CDP. For each dataset, algorithm and k, we averaged Cost(T , S, Tk) over 30 runs. [...] In all of the experiments, the points were normalized to have a maximal norm of 1/2. |