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..
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
Authors: Geert-Jan Huizing, Laura Cantini, Gabriel Peyré
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we showcase Wasserstein Singular Vectors on a single-cell RNA-sequencing dataset. Section 5 demonstrates the potential of Wasserstein Singular Vectors compared to ad-hoc applications of Optimal Transport, by studying a single-cell RNA-sequencing dataset. |
| Researcher Affiliation | Academia | 1D epartement de math ematiques et applications de l Ecole Normale Sup erieure, CNRS, Ecole Normale Sup erieure, Universit e PSL, 75005, Paris, France 2Computational Systems Biology Team, Institut de Biologie de l Ecole Normale Sup erieure, CNRS, INSERM, Ecole Normale Sup erieure, Universit e PSL, 75005, Paris, France. |
| Pseudocode | No | The paper describes algorithms in prose and mathematical formulations but does not include explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | A Python package implementing all algorithms in this paper is available at github.com/gjhuizing/wsingular. |
| Open Datasets | Yes | A commonly analyzed sc RNA-seq dataset is the PBMC 3k dataset produced by 10X Genomics, obtained through the function pbmc3k of Scanpy (Wolf et al., 2018). |
| Dataset Splits | No | The paper describes the dataset and its preprocessing but does not explicitly state train/validation/test splits or cross-validation methodology for the experiments. |
| Hardware Specification | Yes | CPU computations were performed on a Dell Latitude 5420 with an 8 core 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz CPU. GPU computations were performed on Nvidia V100 SXM2 32 Go GPUs. |
| Software Dependencies | No | The paper mentions software like 'Python', 'POT library', and 'Scikit-learn' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Wasserstein Singular Vectors (τ = 0.001, ε = 0.1), approached by 15 power iterations |