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..
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
Authors: Meyer Scetbon, Gabriel Peyré, Marco Cuturi
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach yields similar results, yet orders of magnitude faster computation than the So TA entropic GW approaches, on both simulated and real data. |
| Researcher Affiliation | Collaboration | Meyer Scetbon 1 CREST-ENSAE Gabriel Peyr e 2 CNRS and ENS, PSL Marco Cuturi 3 CREST-ENSAE, work partly done at Google, currently at Apple. Correspondence to: meyer scetbon <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: Entropic-GW |
| Open Source Code | Yes | The code is available at https://github.com/meyerscetbon/Linear Gromov. |
| Open Datasets | Yes | Experiments were run on a Mac Book Pro 2019 laptop, and data from github.com/rsinghlab/SCOT. The code is available at https://github.com/meyerscetbon/Linear Gromov. |
| Dataset Splits | No | The paper mentions synthetic and real datasets, and specific sample sizes for experiments (e.g., 'n = m = 1000 samples'), but it does not provide explicit training/validation/test split percentages or sample counts for these splits. |
| Hardware Specification | Yes | Experiments were run on a Mac Book Pro 2019 laptop |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | In all other experiments, we always set γ = 100 and α = 10 10 for our methods, and only focus on rank r. |