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..
A User's Guide to Sampling Strategies for Sliced Optimal Transport
Authors: Keanu Sisouk, Julie Delon, Julien Tierny
TMLR 2025 | Venue PDF | 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. |