Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates
Authors: Rémi Leluc, Aymeric Dieuleveut, François Portier, Johan Segers, Aigerim Zhuman
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Several numerical experiments demonstrate the superior performance of SHCV against state-of-the-art methods for SW distance computation. |
| Researcher Affiliation | Academia | 1CMAP, Ecole Polytechnique, Institut Polytechnique de Paris, France 2CREST, ENSAI, France 3ISBA/LIDAM, UCLouvain, Belgium. |
| Pseudocode | Yes | Algorithm 1 Sliced Wasserstein Monte Carlo |
| Open Source Code | Yes | For ease of reproducibility, numerical details are in the appendix and the code is available here. |
| Open Datasets | Yes | Then we focus on SW2 2(µm, νm) between 3D point clouds from the Shape Net Core dataset of Chang et al. (2015)... we consider the image classification task on the digits dataset of Alpaydin & Kaynak (1998). |
| Dataset Splits | No | We use a train/test split of size 80/20 giving Ntrain = 1 437 training and Ntest = 360 testing images, respectively, where each set is balanced. No explicit validation split is mentioned. |
| Hardware Specification | Yes | The experiments were performed on a laptop Intel Core i7-10510U CPU 1.80GHz 8. |
| Software Dependencies | Yes | In Algorithm 8, random orthogonal matrices R U(Od(R)) are generated using the function ortho group of the Python package scipy (Virtanen et al., 2020). |
| Experiment Setup | Yes | The degrees of the spherical harmonics are reported in Table 9 in Appendix D.2. ... The SHCV estimate uses spherical harmonics up to degree 2L = 16 leading to s = 152 control variates. |