Screening Sinkhorn Algorithm for Regularized Optimal Transport
Authors: Mokhtar Z. Alaya, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the efficiency of SCREENKHORN on complex tasks such as dimensionality reduction and domain adaptation involving regularized optimal transport. |
| Researcher Affiliation | Collaboration | Mokhtar Z. Alaya LITIS EA4108 University of Rouen Normandy mokhtarzahdi.alaya@gmail.com Maxime Bérar LITIS EA4108 University of Rouen Normandy maxime.berar@univ-rouen.fr Gilles Gasso LITIS EA4108 INSA, University of Rouen Normandy gilles.gasso@insa-rouen.fr Alain Rakotomamonjy LITIS EA4108 University of Rouen Normandy and Criteo AI Lab, Criteo Paris alain.rakoto@insa-rouen.fr |
| Pseudocode | Yes | Algorithm 1: SCREENKHORN(C, η, µ, ν, nb, mb) |
| Open Source Code | No | The paper states that the authors 'have implemented our SCREENKHORN algorithm in Python' and 'based our code on the ones of Python Optimal Transport toolbox (POT) [15]' but does not provide a link or explicit statement that their specific implementation of SCREENKHORN is open-source or publicly available. |
| Open Datasets | Yes | We have implemented our SCREENKHORN algorithm in Python and used the L-BFGS-B of Scipy. Regarding the machine-learning based comparison, we have based our code on the ones of Python Optimal Transport toolbox (POT) [15] and just replaced the sinkhorn function call with a screenkhorn one. (...) Here, we analyse the impact of using SCREENKHORN instead of SINKHORN in a complex machine learning pipeline. Our two applications are a dimensionality reduction technique, denoted as Wasserstein Discriminant Analysis (WDA), based on Wasserstein distance approximated through Sinkhorn divergence [16] and a domain-adaptation using optimal transport mapping [10], named OTDA. (...) We have used a toy problem involving Gaussian classes with 2 discriminative features and 8 noisy features and the MNIST dataset. |
| Dataset Splits | No | The paper mentions 'different values of η, the regularization parameter, the allowed budget nb ranging from 0.01 to 0.99, different values of n and m' and measures 'marginal violations', 'running time', and 'relative divergence difference'. However, it does not specify explicit train/validation/test splits with percentages, absolute counts, or references to predefined standard splits for the datasets used. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory specifications, or types of computing resources used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Python', 'L-BFGS-B of Scipy', and 'Python Optimal Transport toolbox (POT) [15]' but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | For all applications, we have set η = 1 unless otherwise specified. (...) for SCREENKHORN, the L-BFGS-B algorithm is stopped when the largest component of the projected gradient is smaller than 10 6, when the number of iterations or the number of objective function evaluations reach 105. |