3 citations · 5 across the 3 of their papers we have counts for
7 papers
Breiman's "Two Cultures" Revisited and Reconciled
Subhadeep, Mukhopadhyay, Kaijun Wang
In a landmark paper published in 2001, Leo Breiman described the tense standoff between two cultures of data modeling: parametric statistical and algorithmic machine learning. The…
Nonparametric Universal Copula Modeling
Subhadeep Mukhopadhyay, Emanuel Parzen
To handle the ubiquitous problem of "dependence learning," copulas are quickly becoming a pervasive tool across a wide range of data-driven disciplines encompassing neuroscience, f…
Spectral Graph Analysis: A Unified Explanation and Modern Perspectives
Subhadeep Mukhopadhyay, Kaijun Wang
Complex networks or graphs are ubiquitous in sciences and engineering: biological networks, brain networks, transportation networks, social networks, and the World Wide Web, to nam…
A Nonparametric Approach to High-dimensional k-sample Comparison Problems
Subhadeep, Mukhopadhyay, Kaijun Wang
High-dimensional k-sample comparison is a common applied problem. We construct a class of easy-to-implement nonparametric distribution-free tests based on new tools and unexplored…
Decentralized Nonparametric Multiple Testing
Subhadeep Mukhopadhyay
Consider a big data multiple testing task, where, due to storage and computational bottlenecks, one is given a very large collection of p-values by splitting into manageable chunks…
Bayesian Modeling via Goodness-of-fit
Subhadeep, Mukhopadhyay, Douglas Fletcher
The two key issues of modern Bayesian statistics are: (i) establishing principled approach for distilling statistical prior that is consistent with the given data from an initial b…