Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …

Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but
they have thus far been held back by limitations in their simulation-based maximum …

Simulation-free methods for training continuous-time generative models construct probability
paths that go between noise distributions and individual data samples. Recent works, such …

Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth
century and has led to a plethora of methods for answering many theoretical and applied …

Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a
tissue slice while recording the two-dimensional (2D) coordinates of each spot. We …

Optimal transport distances have found many applications in machine learning for their
capacity to compare non-parametric probability distributions. Yet their algorithmic complexity …

Missing data is a crucial issue when applying machine learning algorithms to real-world
datasets. Starting from the simple assumption that two batches extracted randomly from the …

Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but
they have thus far been held back by limitations in their simulation-based maximum …

We present simulation-free score and flow matching ([SF] $^ 2$ M), a simulation-free
objective for inferring stochastic dynamics given unpaired samples drawn from arbitrary …

Tabular data is hard to acquire and is subject to missing values. This paper introduces a
novel approach for generating and imputing mixed-type (continuous and categorical) tabular …