5 citations · 10 across the 13 of their papers we have counts for
13 papers
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…
The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research
Jasin Machkour, Michael Muma, Daniel P. Palomar
Modern genomics research relies on genome-wide association studies (GWAS) to identify the few genetic variants among potentially millions that are associated with diseases of inter…
False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks
Jasin Machkour, Michael Muma, Daniel P. Palomar
Genomics biobanks are information treasure troves with thousands of phenotypes (e.g., diseases, traits) and millions of single nucleotide polymorphisms (SNPs). The development of m…
Solving FDR-Controlled Sparse Regression Problems with Five Million Variables on a Laptop
Fabian Scheidt, Jasin Machkour, Michael Muma
Currently, there is an urgent demand for scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods to ensure the repro-ducibility…
High-Dimensional False Discovery Rate Control for Dependent Variables
Jasin Machkour, Michael Muma, Daniel P. Palomar
Algorithms that ensure reproducible findings from large-scale, high-dimensional data are pivotal in numerous signal processing applications. In recent years, multivariate false dis…
FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking
Jasin Machkour, Daniel P. Palomar, Michael Muma
In high-dimensional data analysis, such as financial index tracking or biomedical applications, it is crucial to select the few relevant variables while maintaining control over th…
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
Taulant Koka, Jasin Machkour, Michael Muma
Gaussian graphical models emerge in a wide range of fields. They model the statistical relationships between variables as a graph, where an edge between two variables indicates con…
Sparse PCA with False Discovery Rate Controlled Variable Selection
Jasin Machkour, Arnaud Breloy, Michael Muma +2
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension. By imposing loading vectors to be sparse, it performs the…
The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control
Jasin Machkour, Michael Muma, Daniel P. Palomar
We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false…
A New Sparse and Robust Adaptive Lasso Estimator for the Independent Contamination Model
Jasin Machkour, Michael Muma, Bastian Alt +1
Many problems in signal processing require finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. When dealing with real-world data, the pre…