Stochastic Optimization for Large-scale Optimal Transport
Authors: Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis Bach
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 genevay@ceremade.dauphine.fr; Marco Cuturi CREST, ENSAE Université Paris-Saclay marco.cuturi@ensae.fr; Gabriel Peyré CNRS and DMA, École Normale Supérieure INRIA Mokaplan project-team gabriel.peyre@ens.fr; Francis Bach INRIA Sierra project-team DI, ENS francis.bach@inria.fr |
| 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]. |