348 citations · 953 across the 15 of their papers we have counts for
16 papers
Sparse reconstruction of ordinary differential equations with inference
Sara Venkatraman, Sumanta Basu, Martin T. Wells
Sparse regression has emerged as a popular technique for learning dynamical systems from temporal data, beginning with the SINDy (Sparse Identification of Nonlinear Dynamics) frame…
An empirical Bayes approach to estimating dynamic models of co-regulated gene expression
Sara Venkatraman, Sumanta Basu, Andrew G. Clark +3
Time-course gene expression datasets provide insight into the dynamics of complex biological processes, such as immune response and organ development. It is of interest to identify…
Graphical models for nonstationary time series
Sumanta Basu, Suhasini Subba Rao
We propose NonStGM, a general nonparametric graphical modeling framework for studying dynamic associations among the components of a nonstationary multivariate time series. It buil…
A Survey of Estimation Methods for Sparse High-dimensional Time Series Models
Sumanta Basu, David S. Matteson
High-dimensional time series datasets are becoming increasingly common in many areas of biological and social sciences. Some important applications include gene regulatory network…
Random Forests for dependent data
Arkajyoti Saha, Sumanta Basu, Abhirup Datta
Random forest (RF) is one of the most popular methods for estimating regression functions. The local nature of the RF algorithm, based on intra-node means and variances, is ideal w…
A Debiased MDI Feature Importance Measure for Random Forests
Xiao Li, Yu Wang, Sumanta Basu +2
Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people rout…