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..
A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization
Authors: Jianbo Ye, James Z. Wang, Jia Li
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We applied the method to optimal transport with Coulomb cost and the Wasserstein non-negative matrix factorization problem, and made comparisons with the existing method of entropy regularization. By experiments, we demonstrate the effectiveness of Gibbs-OT for solving optimal transport with Coulomb cost (Benamou et al., 2016) and the Wasserstein non-negative matrix factorization (NMF) problem (Sandler & Lindenbaum, 2009; Rolet et al., 2016). |
| Researcher Affiliation | Academia | 1College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA. 2Department of Statistics, The Pennsylvania State University, University Park, PA.. Correspondence to: Jianbo Ye <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Gibbs Sampling for Optimal Transport |
| Open Source Code | No | The paper mentions 'a C/C++ implementation' but does not provide any statement about open-sourcing the code or a link to a repository for the described methodology. |
| Open Datasets | Yes | one is a subset of MNIST handwritten digit images which contains 200 digits of 5 , and the other is the ORL 400-face dataset. |
| Dataset Splits | No | The paper mentions using datasets (MNIST, ORL) but does not specify any explicit train/validation/test dataset splits or reference predefined splits with citations. |
| Hardware Specification | Yes | On a single-core of a 3.3 GHz Intel Core i5 CPU, the average time spent for each epoch for these two datasets are 0.84 seconds and 16.8 seconds, respectively. |
| Software Dependencies | No | The paper mentions 'a C/C++ implementation' and 'Mosek' as a solver but does not provide specific version numbers for any software components. |
| Experiment Setup | Yes | For Gibbs-OT, we use a geometric temperature scheme such that T = 2.0(1/l4)n/l/N at the n-th iteration, where l is the max iteration number. In particular, we set K = 40, γ = 2.0 for both datasets. |