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..
On Scalable and Efficient Computation of Large Scale Optimal Transport
Authors: Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on both synthetic and real datasets illustrate that SPOT is robust and has favorable convergence behavior. |
| Researcher Affiliation | Academia | Yujia Xie 1 Minshuo Chen 1 Haoming Jiang 1 Tuo Zhao 1 Hongyuan Zha 1 1Georgia Tech. |
| Pseudocode | Yes | Algorithm 1 Mini-batch Primal Dual Stochastic Gradient Algorithm for SPOT |
| Open Source Code | No | The paper does not include a statement that its own source code is being released or provide a link to its repository. It only links to the code of a comparison method (Deep JDOT). |
| Open Datasets | Yes | We evaluate DASPOT with the MNIST, MNISTM, USPS (Hull, 1994), and SVHN (Netzer et al., 2011) datasets. |
| Dataset Splits | No | The paper does not specify precise dataset split percentages or sample counts for training, validation, and testing. It refers to using datasets for tasks like 'Source Domain -> Target Domain' but not the specific partitioning methodology. |
| Hardware Specification | Yes | All experiments are implemented with Py Torch using one GTX1080Ti GPU and a Linux desktop computer with 32GB memory |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | We adopt the Adam optimizer with configuration parameters 0.5 and 0.999 (Kingma & Ba, 2014). [...] We set da = 0 for the first 105 iteration to wait the generators to be well trained. Then we set da = 10 for the next 3 105 iteration. We take totally 4 105 iterations, and set the learning rate equal to 10 4 and batch size equal to 128 for all experiments. |