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..
Dimensionality Reduction for Wasserstein Barycenter
Authors: Zachary Izzo, Sandeep Silwal, Samson Zhou
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, our experimental results validate the speedup provided by dimensionality reduction while maintaining solution quality. Finally, we present experimental evaluation of our proposed methodology. Our experiments in Section 7 demonstrate that on natural datasets, we can reduce the dimension by 1-2 orders of magnitude while increasing the solution cost by only 5%. |
| Researcher Affiliation | Academia | Zachary Izzo Stanford University EMAIL Sandeep Silwal MIT EMAIL Samson Zhou Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Using dimensionality reduction with any algorithm A for computing WB |
| Open Source Code | No | The paper states: "We use the code and default settings from [Ye19] to compute the Wasserstein barycenter". While [Ye19] refers to a GitHub repository, it is external code used by the authors, not the authors' own implementation or open-source release of the methodology described in this paper. |
| Open Datasets | Yes | FACES dataset: This dataset is used in the influential ISOMAP paper and consists of 698 images of faces in dimension 4096 [TSL00]. MNIST dataset: We subsample 104 images from the MNIST test dataset (dimension 784). |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test splits. It mentions forming distributions from datasets but not how these distributions were split for training or validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Wbc-matlab" from [Ye19] but does not specify the version numbers for MATLAB or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | We project our datasets in dimensions d ranging from d = 2 to d = 30 and compute the Wasserstein barycenter for p = 2. For FACES, we limit the support size of the barycenter to be at most 5 points in R4096... For MNIST we limit the support size of the barycenter to be at most 40. |