134 citations · 304 across the 14 of their papers we have counts for
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Large Scale Partial Correlation Screening with Uncertainty Quantification
Emily Neo, Peter Radchenko, Bala Rajaratnam
Identifying multivariate dependencies in high-dimensional data is an important problem in large-scale inference. This problem has motivated recent advances in mining (partial) corr…
Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives
Hussein Hazimeh, Rahul Mazumder, Peter Radchenko
We present a new algorithmic framework for grouped variable selection that is based on discrete mathematical optimization. While there exist several appealing approaches based on c…
Irrational Exuberance: Correcting Bias in Probability Estimates
Gareth M. James, Peter Radchenko, Bradley Rava
We consider the common setting where one observes probability estimates for a large number of events, such as default risks for numerous bonds. Unfortunately, even with unbiased es…
Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low
Rahul Mazumder, Peter Radchenko, Antoine Dedieu
We study a seemingly unexpected and relatively less understood overfitting aspect of a fundamental tool in sparse linear modeling - best subset selection, which minimizes the resid…
Feature Screening in Large Scale Cluster Analysis
Trambak Banerjee, Gourab Mukherjee, Peter Radchenko
We propose a novel methodology for feature screening in clustering massive datasets, in which both the number of features and the number of observations can potentially be very lar…
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization
Rahul Mazumder, Peter Radchenko
We propose a novel high-dimensional linear regression estimator: the Discrete Dantzig Selector, which minimizes the number of nonzero regression coefficients subject to a budget on…