1 code implementation • 27 Jun 2021 • James D. Wilson, Jihui Lee
We consider the problem of interpretable network representation learning for samples of network-valued data.
2 code implementations • 8 Jun 2020 • Terence Parr, James D. Wilson, Jeff Hamrick
In this paper, we give mathematical definitions of feature impact and importance, derived from partial dependence curves, that operate directly on the data.
1 code implementation • 15 Jul 2019 • Terence Parr, James D. Wilson
Partial dependence curves (FPD) introduced by Friedman, are an important model interpretation tool, but are often not accessible to business analysts and scientists who typically lack the skills to choose, tune, and assess machine learning models.
1 code implementation • 17 Sep 2018 • James D. Wilson, Melanie Baybay, Rishi Sankar, Paul Stillman
Learning interpretable features from complex multilayer networks is a challenging and important problem.
Social and Information Networks Physics and Society
1 code implementation • 12 Jun 2017 • Kelsey MacMillan, James D. Wilson
Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents.
no code implementations • 3 Dec 2014 • James D. Wilson, Simi Wang, Peter J. Mucha, Shankar Bhamidi, Andrew B. Nobel
In addition, we carry out a simulation study to assess the effectiveness of ESSC in networks with various types of community structure, including networks with overlapping communities and those with background vertices.