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..
Optimal Transport with Cyclic Symmetry
Authors: Shoichiro Takeda, Yasunori Akagi, Naoki Marumo, Kenta Niwa
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show the effectiveness of our algorithms for LOT and EROT in synthetic/real-world data that has a strict/approximate cyclic symmetry structure. To validate the effectiveness of our algorithms, we conducted experiments on synthetic/real-world data that satisfy Assumption 1 strictly/approximately. |
| Researcher Affiliation | Industry | NTT Corporation, 1-1 Hikari-no-oka, Yokosuka-Shi, Kanagawa, 239-0847, Japan |
| Pseudocode | Yes | Algorithm 1: Fast Algorithm for C-LOT, Algorithm 2: Cyclic Sinkhorn Algorithm for C-EROT, Algorithm 3: Two-Stage Sinkhorn Algorithm for C-EROT with Approximate Cyclic Symmetry |
| Open Source Code | No | The paper states 'All the codes were implemented in Python.' but does not provide any explicit statement about code availability or a link to a repository. |
| Open Datasets | Yes | We tested our algorithms on the real-world case of mirror symmetry (n = 2) in Example 1 with the NYU Symmetry Database (Cicconet et al. 2017). |
| Dataset Splits | No | The paper does not explicitly provide specific training/test/validation dataset splits or cross-validation details for their experiments. |
| Hardware Specification | Yes | These experiments were performed on a Windows laptop with Intel Core i7-10750H CPU, 32 GB memory. |
| Software Dependencies | No | The paper mentions 'All the codes were implemented in Python.' and 'The network simplex algorithm was implemented using LEMON (Dezs o, J uttner, and Kov acs 2011).' but does not provide specific version numbers for Python or LEMON. |
| Experiment Setup | Yes | We set λ = 0.5 for the regularizer (3). we first run the cyclic Sinkhorn algorithm until the marginal error || (diag(bp)Kdiag(bq)) 1m β||2 is below 1.0 10 3 and then run the Sinkhorn algorithm until the difference between its objective function value and the value obtained by directly solving C-EROT with the Sinkhorn algorithm is below 1.0 10 4. |