Faster Wasserstein Distance Estimation with the Sinkhorn Divergence
Authors: Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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.' |