12 citations · 19 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Beyond kNN: Adaptive, Sparse Neighborhood Graphs via Optimal Transport
Tetsuya Matsumoto, Stephen Zhang, Geoffrey Schiebinger
Nearest neighbour graphs are widely used to capture the geometry or topology of a dataset. One of the most common strategies to construct such a graph is based on selecting a fixed…
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…
Towards a mathematical theory of trajectory inference
Hugo Lavenant, Stephen Zhang, Young-Heon Kim +1
We devise a theoretical framework and a numerical method to infer trajectories of a stochastic process from samples of its temporal marginals. This problem arises in the analysis o…