2 citations · 2 across the 5 of their papers we have counts for
5 papers
Mixed Membership Models for Multilevel Functional Data
Donatello Telesca, Nicholas Marco, Emma Landry
Mixed membership models extend classical clustering by substituting the notion of uncertain membership with the notion of mixed membership. In particular, these models allow each o…
Modeling Neural Switching via Drift-Diffusion Models
Nicholas Marco, Jennifer M. Groh, Surya T. Tokdar
Neural encoding is a field in neuroscience that focuses on characterizing how information from stimuli is encoded in the spiking activity of neurons. When more than one stimulus is…
Covariate Adjusted Functional Mixed Membership Models
Nicholas Marco, Damla Şentürk, Shafali Jeste +3
Mixed membership models are a flexible class of probabilistic data representations used for unsupervised and semi-supervised learning, allowing each observation to partially belong…
Flexible Regularized Estimation in High-Dimensional Mixed Membership Models
Nicholas Marco, Damla Şentürk, Shafali Jeste +3
Mixed membership models are an extension of finite mixture models, where each observation can partially belong to more than one mixture component. A probabilistic framework for mix…
Functional Mixed Membership Models
Nicholas Marco, Damla Şentürk, Shafali Jeste +3
Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this paper, we pr…