3 papers
stat.ML2023
Manifold Learning with Sparse Regularised Optimal Transport
Stephen Zhang, Gilles Mordant, Tetsuya Matsumoto +1
Manifold learning is a central task in modern statistics and data science. Many datasets (cells, documents, images, molecules) can be represented as point clouds embedded in a high…
math.AP2022
Quadratically Regularized Optimal Transport: nearly optimal potentials and convergence of discrete Laplace operators
Gilles Mordant, Stephen Zhang
We consider the conjecture proposed in Matsumoto, Zhang and Schiebinger (2022) suggesting that optimal transport with quadratic regularisation can be used to construct a graph whos…
stat.ML2021
A unified framework for non-negative matrix and tensor factorisations with a smoothed Wasserstein loss
Stephen Y. Zhang
Non-negative matrix and tensor factorisations are a classical tool for finding low-dimensional representations of high-dimensional datasets. In applications such as imaging, datase…