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..
Federated t-SNE and UMAP for Distributed Data Visualization
Authors: Dong Qiao, Xinxian Ma, Jicong Fan
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple datasets demonstrate that, compared to the original algorithms, the accuracy drops of our federated algorithms are tiny. |
| Researcher Affiliation | Academia | School of Data Science, The Chinese University of Hong Kong, Shenzhen, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Federated Distribution Learning |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We applied the proposed Fed-t SNE and Fed-UMAP methods to the MNIST and Fashion-MNIST datasets, with m X = 40, 000, and set n Y = 500. Additionally, "We utilized three datasets MNIST, COIL-20, and Mice Protein (detailed in Appendix) to evaluate the effectiveness of our Fed-Spe Clust." |
| Dataset Splits | Yes | We designed the experiment with 10 clients, where IID (independent and identically distributed) refers to each client s data being randomly sampled from the MNIST dataset, thus including all classes. In contrast, non-IID means that each client s data contains only a single class. |
| Hardware Specification | No | The paper does not provide specific hardware details 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 experiment. |
| Experiment Setup | Yes | We applied the proposed Fed-t SNE and Fed-UMAP methods to the MNIST and Fashion-MNIST datasets, with m X = 40, 000, and set n Y = 500. We designed the experiment with 10 clients, where IID (independent and identically distributed) refers to each client s data being randomly sampled from the MNIST dataset, thus including all classes. In contrast, non-IID means that each client s data contains only a single class. In Figure 2, the relevant metrics reached convergence after approximately 50 epochs. The noise level β controls the scale of noise, with each element of noise E being drawn from N(0, β2sd2( fp(Yp))). |