Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Large-scale optimal transport map estimation using projection pursuit
Authors: Cheng Meng, Yuan Ke, Jingyi Zhang, Mengrui Zhang, Wenxuan Zhong, Ping Ma
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,943 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,950 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |