504 citations · 2k across the 109 of their papers we have counts for
6 papers · 1 filter
Learning sources of variability from high-dimensional observational studies
Eric W. Bridgeford, Jaewon Chung, Brian Gilbert +6
Causal inference studies whether the presence of a variable influences an observed outcome. As measured by quantities such as the "average treatment effect," this paradigm is emplo…
Semisupervised regression in latent structure networks on unknown manifolds
Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset +2
Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is assoc…
Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror
Tianyi Chen, Youngser Park, Ali Saad-Eldin +10
Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids…
Polarity is all you need to learn and transfer faster
Qingyang Wang, Michael A. Powell, Ali Geisa +2
Natural intelligences (NIs) thrive in a dynamic world - they learn quickly, sometimes with only a few samples. In contrast, artificial intelligences (AIs) typically learn with a pr…
Preserving Derivative Information while Transforming Neuronal Curves
Thomas L. Athey, Daniel J. Tward, Ulrich Mueller +3
The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more in…
Approximately optimal domain adaptation with Fisher's Linear Discriminant
Hayden S. Helm, Ashwin De Silva, Joshua T. Vogelstein +2
We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation. The class is the convex combination of two hypotheses: i) an average h…