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..
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence
Authors: Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We finally demonstrate the efficiency of the proposed estimators with numerical experiments. |
| Researcher Affiliation | Academia | 1: Laboratoire de Mathématiques d Orsay, CNRS, Université Paris-Saclay, Orsay, France 2: ENS, PSL University, Paris, France 3: Univ. Gustave Eiffel, CNRS, ESIEE Paris, Marne-la-Vallée, France |
| Pseudocode | No | The paper describes Sinkhorn's algorithm using mathematical equations, but it does not present it in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | 2The code to reproduce these experiments is available at this webpage https://gitlab.com/ proussillon/wasserstein-estimation-sinkhorn-divergence. |
| Open Datasets | No | The paper assesses the estimators on 'synthetic problems' using 'n independent samples from µ and ν' or 'densities discretized on grids', but does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper discusses 'n independent samples' or 'discretized densities' for the distributions µ and ν, but it does not specify explicit dataset splits (e.g., percentages or counts) for training, validation, or testing to reproduce the experiments. The concept of train/validation/test splits in the context of machine learning model training is not applied to the data used in this paper. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | In Section 5, the paper states: 'for a target L1 error on the potential, we chose the largest λ and smallest n that achieve this error, with λ [0.1, 1] and n [10, 100000]' and 'We report the computational time using the Sinkhorn s iterations of Eq. (6) stopped when the ℓ1-error on the marginals is below 10 5.' |