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..
Stochastic Optimization for Large-scale Optimal Transport
Authors: Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis Bach
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We backup these claims on a set of discrete, semi-discrete and continuous benchmark problems.Numerical Illustrations on Bags of Word-Embeddings.Numerical Illustrations. Simulations are performed in X = Y = R3. |
| Researcher Affiliation | Academia | Aude Genevay CEREMADE, Université Paris-Dauphine INRIA Mokaplan project-team EMAIL; Marco Cuturi CREST, ENSAE Université Paris-Saclay EMAIL; Gabriel Peyré CNRS and DMA, École Normale Supérieure INRIA Mokaplan project-team EMAIL; Francis Bach INRIA Sierra project-team DI, ENS EMAIL |
| Pseudocode | Yes | Algorithm 1 SAG for Discrete OT; Algorithm 2 Averaged SGD for Semi-Discrete OT; Algorithm 3 Kernel SGD for continuous OT |
| Open Source Code | No | The paper does not provide any statement or link for concrete access to source code for the described methodology. |
| Open Datasets | Yes | We use Glove word embeddings [14] to represent words, namely X = Y = R300. |
| Dataset Splits | No | The paper describes sampling of data (e.g., 'sample N = 20, 000 words'), but does not provide specific dataset split information (train/validation/test percentages or counts) needed to reproduce data partitioning for model training or evaluation. |
| Hardware Specification | Yes | We used 4 Tesla K80 cards to compute both SAG and Sinkhorn results. |
| Software Dependencies | No | The paper mentions algorithms like Sinkhorn and SAG, but does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We set ε to 0.01 (other values are considered in the supplementary material).Following the recommendations in [19], SAG s stepsize is tested for 3 different settings, 1/L, 3/L and 5/L.We used minibatches of size 200 for SAG.SGD for 107 iterations.Each mixture is composed of three gaussians whose means are drawn randomly in [0, 1]3, and their correlation matrices are constructed as Σ = 0.01(RT + R) + 3I3 where R is 3 3 with random entries in [0, 1]. |