A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization
Authors: Jianbo Ye, James Z. Wang, Jia Li
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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 <jxy198@ist.psu.edu>. |
| 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. |