4 papers
A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data
Mary Chriselda Antony Oliver, Kaitlyn Hohmeier, Tuyen Tran +3
High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeat…
Wasserstein Mahalanobis Distances for Recovering Latent Geometry
Chuxiangbo Wang, Shiying Li, Caroline Moosmüller
The Mahalanobis distance is a fundamental covariance-adapted metric for multivariate data and plays a central role in recovering latent geometry from nonlinear observations. We ext…
Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors
Amartya Banerjee, Xingyu Xu, Caroline Moosmüller +1
In an inverse problem, the goal is to recover an unknown parameter (e.g., an image) that has typically undergone some lossy or noisy transformation during measurement. Recently, de…
Manifold learning in Wasserstein space
Keaton Hamm, Caroline Moosmüller, Bernhard Schmitzer +1
This paper aims at building the theoretical foundations for manifold learning algorithms in the space of absolutely continuous probability measures $\mathcal{P}_{\mathrm{a.c.}}(Ω)…