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..
Distributional Convergence of the Sliced Wasserstein Process
Authors: Jiaqi Xi, Jonathan Niles-Weed
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Simulation Studies We illustrate our distributional limit results in Monte Carlo simulations. Specifically, we investigate the speed of convergence of the sliced Wasserstein distance and the max-sliced Wasserstein distance. We also investigate the convergence speed of the amplitude, which provides an example of a functional not covered in prior work. |
| Researcher Affiliation | Academia | Jiaqi Xi1 and Jonathan Niles-Weed1,2 1Courant Institute of Mathematical Sciences, New York University, NY 10012 2Center for Data Science, New York University, NY 10011 |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | 3. (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] We have included the code and instructions in the supplemental material. |
| Open Datasets | No | The paper describes distributions P and Q (e.g., 'uniform on unit sphere S2', 'uniform on the surface of ellipsoid') for its simulations but does not provide access information (link, DOI, specific repository, or formal citation with authors/year) for a publicly available dataset that adheres to the strict criteria. |
| Dataset Splits | No | The paper describes sampling i.i.d. observations with specified sizes (e.g., 'n = 50, 100, 500') and repetition counts for Monte Carlo simulations, but it does not specify explicit training/validation/test dataset splits with percentages, sample counts, or citations to predefined splits. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, processor types, or memory amounts) used for running experiments were provided. |
| Software Dependencies | No | The paper mentions using 'the Python package POT [18]' and a 'Riemannian optimization method proposed in [23]' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The paper specifies experimental parameters such as sample sizes ('n = 50, 100, 500' and 'n = 1000') and the number of repetitions for Monte Carlo simulations ('500 times', '2000 times', 'B = 500 replications'). |