6 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…
Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces
Taulant Koka, Manolis C. Tsakiris, Benjamín Béjar Haro +1
Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels. This work expands the cross-channel…
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…
Shuffled Multi-Channel Sparse Signal Recovery
Taulant Koka, Manolis C. Tsakiris, Michael Muma +1
Mismatches between samples and their respective channel or target commonly arise in several real-world applications. For instance, whole-brain calcium imaging of freely moving orga…