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..
Approximate Optimal Transport for Continuous Densities with Copulas
Authors: Jinjin Chi, Jihong Ouyang, Ximing Li, Yang Wang, Meng Wang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on real applications of image retrieval and synthetic data demonstrate that our Cop-OT can gain more accurate approximations to continuous OT values than the state-of-the-art baselines. ... In this section, we empirically evaluate Cop-OT on both synthetic and real data. |
| Researcher Affiliation | Academia | Jinjin Chi1,2 , Jihong Ouyang1,2 , Ximing Li1,2 , Yang Wang3 and Meng Wang3 1 College of Computer Science and Technology, Jilin University, China 2 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, China 3 School of Computer Science and Information Engineering, Hefei University of Technology, China |
| Pseudocode | Yes | Algorithm 1 Optimization of Cop-OT |
| Open Source Code | Yes | The derivation details of Eq.11 can be found at https://github.com/jinjinchi/Approximate-Optimal-Transport-for-Continuous-Densities-with-Copulas. |
| Open Datasets | Yes | The MINST5, a dataset of handwritten digits from zero to nine, is used. We randomly select 10,000 images as the database and 150 images for texting. ... 5http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | No | The paper states using 10,000 images as a database and 150 for testing, but does not specify training, validation, or test splits beyond that. It does not mention cross-validation or specific percentages for these splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU/CPU models, memory, or cloud instances. It only mentions the "expensive computational cost on neural networks" in a general sense. |
| Software Dependencies | No | The paper mentions the Adam method [Kingma and Ba, 2015] and Vine Copula, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For Cop-OT, we employ the family of Gaussian copula function, and use the standard Gaussians as the mapping distribution in the reparameterization trick. For all methods. the sample number S is set to 200, and we report the average results of five independent runs. ... We use the Adam method [Kingma and Ba, 2015] to adaptively adjust the optimization process... in this work we empirically fixed the parameters of Adam as follows: β1 = 0.9, β2 = 0.999 and α = 0.001. |