34 citations · 76 across the 9 of their papers we have counts for
9 papers
Simulation-free Schrödinger bridges via score and flow matching
Alexander Tong, Nikolay Malkin, Kilian Fatras +5
We present simulation-free score and flow matching ([SF]M), a simulation-free objective for inferring stochastic dynamics given unpaired samples drawn from arbitrary source and…
Graph Fourier MMD for Signals on Graphs
Samuel Leone, Aarthi Venkat, Guillaume Huguet +3
While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such…
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
Guillaume Huguet, Alexander Tong, Edward De Brouwer +4
Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Su…
Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong, Kilian Fatras, Nikolay Malkin +5
Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their simulation-based maximum likelihood traini…
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds
Guillaume Huguet, Alexander Tong, María Ramos Zapatero +3
Efficient computation of optimal transport distance between distributions is of growing importance in data science. Sinkhorn-based methods are currently the state-of-the-art for su…
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Guillaume Huguet, D. S. Magruder, Alexander Tong +4
We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) that learns stochastic, continuous population dynamics from static snapshot samples taken at spor…
Time-inhomogeneous diffusion geometry and topology
Guillaume Huguet, Alexander Tong, Bastian Rieck +5
Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifo…
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong, Guillaume Huguet, Dennis Shung +5
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observations in many domains. Further, in many case…
Diffusion Earth Mover's Distance and Distribution Embeddings
Alexander Tong, Guillaume Huguet, Amine Natik +5
We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mover's Distance (EMD). We model the datas…