Code & Software production

You can find a list of some of the softwares and codes available from my research here. If you incorporate it into your research, kindly cite the corresponding paper.

Software

  • Together with Rémi Flamary , I am one of the creator of the Python Optimal Transport toolbox (POT) for promoting research works in computational optimal transport. Unfortunately, I have not much time now to help in maintaining the library, but we (with my students) are still contributing, when possible, to novel features.

Python optimal transport
To install it, simply pip install pot

  • TorchDR is an open-source dimensionality reduction (DR) library using PyTorch. Its goal is to accelerate the development of new DR methods by providing a common simplified framework. It was mainly created by Hugues Van Assel, and I am contributing to it.
Torch Dimensionality Reduction

Paper codes

I am deeply committed to reproducible research. In general, most of the codes associated to my papers are available online, if not listes in the following:

  • Non Euclidean Sliced Optimal Transport Sampling [Code]
  • Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds [Code]
  • SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities [Code]
  • Time Series Alignment with Global Invariances [Code]
  • Efficient Gradient Flows in Sliced-Wasserstein Space [Code]
  • Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals [Code]
  • Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections [Code]
  • Semi-relaxed Gromov-Wasserstein divergence for graphs classification [Code]
  • Template based Graph Neural Network with Optimal Transport Distances [Code]
  • Aligning individual brains with Fused Unbalanced Gromov-Wasserstein [Code]
  • Spherical Sliced Wasserstein [Code]
  • Semi-relaxed Gromov Wasserstein divergence with applications on graphs [Code]
  • Optimal Transport for Conditional Domain Matching and Label Shift [Code]
  • Online Graph Dictionary Learning [Code]
  • Unbalanced minibatch Optimal Transport; applications to Domain Adaptation [Code]
  • POT: Python Optimal Transport [Code]
  • Generating natural adversarial Remote Sensing Images [Code]
  • Wasserstein Adversarial Regularization for learning with label noise [Code]
  • CO-Optimal Transport [Code]
  • Fused Gromov-Wasserstein Distance for Structured Objects [Code]
  • Sliced Gromov-Wasserstein [Code]
  • Optimal Transport for Multi-source Domain Adaptation under Target Shift [Code]
  • DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation [Code]
  • Large-Scale Optimal Transport and Mapping Estimation [Code]
  • Learning Wasserstein Embeddings [Code]
  • Wasserstein Discriminant Analysis [Code]
  • Joint Distribution Optimal Transportation for Domain Adaptation [Code]
  • Optimal spectral transportation with application to music transcription [Code]
  • Mapping estimation for discrete optimal transport [Code]
  • Optimal transport for domain adaptation [Code]
  • Supervised planetary unmixing with optimal transport [Code]