21 citations · 45 across the 11 of their papers we have counts for
4 papers · 1 filter
Kan Extensions in Data Science and Machine Learning
Dan Shiebler
A common problem in data science is "use this function defined over this small set to generate predictions over that larger set." Extrapolation, interpolation, statistical inferenc…
Category Theory in Machine Learning
Dan Shiebler, Bruno Gavranović, Paul Wilson
Over the past two decades machine learning has permeated almost every realm of technology. At the same time, many researchers have begun using category theory as a unifying languag…
Flattening Multiparameter Hierarchical Clustering Functors
Dan Shiebler
We bring together topological data analysis, applied category theory, and machine learning to study multiparameter hierarchical clustering. We begin by introducing a procedure for…
Functorial Manifold Learning
Dan Shiebler
We adapt previous research on category theory and topological unsupervised learning to develop a functorial perspective on manifold learning, also known as nonlinear dimensionality…