37 citations · 48 across the 9 of their papers we have counts for
10 papers
Just add noise: Debiasing tree-based variable importance in mixed data
Jiahe Li, Omar Melikechi
Variable importance scores from tree-based methods such as random forests favor continuous predictors over categorical ones. We present a theoretical analysis of this bias and prop…
Causal Stability Selection
Falco J. Bargagli-Stoffi, Omar Melikechi
Identifying covariates that modify treatment effects is a central problem in causal inference. Yet existing data-adaptive procedures do not provide finite-sample control over the e…
Local graph estimation with pathwise false discovery control
Omar Melikechi, David B. Dunson, Noureddine Melikechi +1
Many datasets include a small set of variables, such as biomarkers or clinical outcomes, whose relationships to the broader system are of primary scientific interest. Estimating th…
Nonparametric IPSS: Fast, flexible feature selection with false discovery control
Omar Melikechi, David B. Dunson, Jeffrey W. Miller
Feature selection is a critical task in machine learning and statistics. However, existing feature selection methods either (i) rely on parametric methods such as linear or general…
Integrated path stability selection
Omar Melikechi, Jeffrey W. Miller
Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of…
Sequential Gibbs Posteriors with Applications to Principal Component Analysis
Steven Winter, Omar Melikechi, David B. Dunson
Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter weighting information in the loss relative to th…
Ellipsoid fitting with the Cayley transform
Omar Melikechi, David B. Dunson
We introduce Cayley transform ellipsoid fitting (CTEF), an algorithm that uses the Cayley transform to fit ellipsoids to noisy data in any dimension. Unlike many ellipsoid fitting…
Random Splitting of Fluid Models: Positive Lyapunov Exponents
Andrea Agazzi, Jonathan C. Mattingly, Omar Melikechi
In this paper we give sufficient conditions for random splitting systems to have a positive top Lyapunov exponent. We verify these conditions for random splittings of two fluid mod…
Random Splitting of Fluid Models: Ergodicity and Convergence
Andrea Agazzi, Jonathan C. Mattingly, Omar Melikechi
We introduce a family of stochastic models motivated by the study of nonequilibrium steady states of fluid equations. These models decompose the deterministic dynamics of interest…
Limits of epidemic prediction using SIR models
Omar Melikechi, Alexander L. Young, Tao Tang +3
The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting the course of an epidemic. In…