12 citations · 19 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Trajectory Inference via Mean-field Langevin in Path Space
Lénaïc Chizat, Stephen Zhang, Matthieu Heitz +1
Trajectory inference aims at recovering the dynamics of a population from snapshots of its temporal marginals. To solve this task, a min-entropy estimator relative to the Wiener me…
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…