A User's Guide to Sampling Strategies for Sliced Optimal Transport
Authors: Keanu Sisouk, Julie Delon, Julien Tierny
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both simulated and real-world data offer a representative comparison of the strategies, culminating in practical recommendations for their best usage. |
| Researcher Affiliation | Academia | Keanu SISOUK EMAIL CNRS, LIP6 Sorbonne University Julie Delon EMAIL MAP5 Paris Cite University Julien Tierny EMAIL CNRS, LIP6 Sorbonne University |
| Pseudocode | No | The paper describes algorithms and methods in detail using mathematical notation and textual explanations, but it does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementations used are grouped and are available here https://github.com/Keanu-Sisouk/SW-Sampling-Guide. |
| Open Datasets | Yes | Extensive experiments on both simulated and real-world data offer a representative comparison of the strategies... We compare different estimates of SW 2 2 (µd, νd) for d {2, 3, 5, 10, 20, 50}... We took three 3D point clouds issued from the Shapenet Core dataset introduced by [Chang et al., 2015]... we select the classical MNIST dataset [Le Cun, 1998]. |
| Dataset Splits | No | The paper describes how specific datasets (Gaussian mixtures, Shapenet Core, MNIST) were constructed or sampled for the experiments (e.g., 'N = 1000' for Gaussian, 'select randomly 600 digit images and divide them into groups of 200' for MNIST). However, it does not specify explicit training/validation/test splits as it primarily focuses on evaluating sampling methods for distance computation rather than training a predictive model. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware used for running the experiments, such as CPU or GPU models, or specific computational resources. |
| Software Dependencies | No | The paper mentions several software components and libraries (e.g., python, scipy.stats.ortho_group, scipy.stats.qmc, POT library), but it does not provide specific version numbers for these dependencies, nor for the Python interpreter itself. |
| Experiment Setup | Yes | Riesz point configuration: We use a code provided by François Clement... where we choose the number of iterations as T = 10, the gradient step as 1 and s = 0.1. Spherical Sliced Wasserstein: For the hyper-parameters we set the number of iteration T = 250, the learning rate ϵ = 150 and the number of great circles L = 500. Spherical Harmonics Control Variates: They provide two possible functions SHCV and SW_CV... we use both functions and always keep only the minimal error among the two. |