Large-scale optimal transport map estimation using projection pursuit
Authors: Cheng Meng, Yuan Ke, Jingyi Zhang, Mengrui Zhang, Wenxuan Zhong, Ping Ma
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, PPMM is computationally easy and converges fast. We assess its finite sample performance through the applications of Wasserstein distance estimation and generative models. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Georgia {cheng.meng25, yuan.ke, jingyi.zhang25, mengrui.zhang, wenxuan, pingma }@uga.edu |
| Pseudocode | Yes | Algorithm 1 Select the most informative projection direction using SAVE; Algorithm 2 Projection pursuit Monge map (PPMM) |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of the source code for the PPMM method described in the paper. It only mentions the R package 'transport' used for comparison methods. |
| Open Datasets | Yes | We study the MNIST dataset, which contains 60,000 training images and 10,000 testing images of hand written digits. The Google Doodle dataset4 https://quickdraw.withgoogle.com/data |
| Dataset Splits | Yes | We randomly split the data into a training set and a validation set of equal sample sizes. |
| Hardware Specification | Yes | The experiments are implemented by an Intel 2.6 GHz processor. |
| Software Dependencies | No | The paper mentions 'R package transport [46]' but does not provide specific version numbers for this or any other critical software dependencies. |
| Experiment Setup | Yes | We set n = 10, 000, d = {10, 20, 50}, µX = 2, µY = 2, ΣX = 0.8|i j|, and ΣY = 0.5|i j|, for i, j = 1, . . . , d. For all three methods, we set the maximum number of iterations to be 200. The dimension of the latent space is set to be 16. we set the termination criteria to be a hard threshold, i.e., 10 5. |