Distributional Convergence of the Sliced Wasserstein Process
Authors: Jiaqi Xi, Jonathan Niles-Weed
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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'). |