37 citations · 144 across the 29 of their papers we have counts for
32 papers
Scalable Variable Selection under Predictor Dependence with Adaptive Virtual Dummies
Taulant Koka, Michael Muma
Reliable high-dimensional variable selection requires scalable error-controlling methods. The Terminating-Random Experiments (T-Rex) selector estimates the false discovery rate (FD…
FDR-Controlled Variable Selection for Generalized Linear Models and Cox Regression with Virtual Dummies
Helena Mehler, Taulant Koka, Michael Muma
In genomics, imaging and clinical studies, only a few of many candidate predictors are often nonlinearly associated with a response that may be, e.g. binary, categorical, a count o…
Virtual Dummies: Enabling Scalable FDR-Controlled Variable Selection via Sequential Sampling of Null Features
Taulant Koka, Jasin Machkour, Daniel P. Palomar +1
High-dimensional variable selection, particularly in genomics, requires error-controlling procedures that scale to millions of predictors. The Terminating-Random Experiments (T-Rex…
FDR Control for Complex-Valued Data with Application in Single Snapshot Multi-Source Detection and DOA Estimation
Fabian Scheidt, Jasin Machkour, Michael Muma
False discovery rate (FDR) control is a popular approach for maintaining the integrity of statistical analyses, especially in high-dimensional data settings, where multiple compari…
Learning False Discovery Rate Control via Model-Based Neural Networks
Arnau Vilella, Jasin Machkour, Michael Muma +1
Controlling the false discovery rate (FDR) in high-dimensional variable selection requires balancing rigorous error control with statistical power. Existing methods with provable g…
Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling
Andrea Gargano, Jasin Machkour, Mimma Nardelli +2
In Affective Computing, a key challenge lies in reliably linking subjective emotional experiences with objective physiological markers. This preliminary study addresses the issue o…