5 papers
We Need to Rethink Benchmarking in Anomaly Detection
Philipp Röchner, Simon Klüttermann, Kevin Kammler +3
Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between…
Deep Transductive Outlier Detection
Simon Klüttermann, Emmanuel Müller
Outlier detection (OD) is one of the core challenges in machine learning. Transductive learning, which leverages test data during training, has shown promise in related machine lea…
Redundancy-aware unsupervised rankings for collections of gene sets
Chiara Balestra, Carlo Maj, Emmanuel Müller +1
The biological roles of gene sets are used to group them into collections. These collections are often characterized by being high-dimensional, overlapping, and redundant families…
Redundancy-aware unsupervised ranking based on game theory -- application to gene enrichment analysis
Chiara Balestra, Carlo Maj, Emmanuel Mueller +1
Gene set collections are a common ground to study the enrichment of genes for specific phenotypic traits. Gene set enrichment analysis aims to identify genes that are over-represen…
Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data
Chiara Balestra, Florian Huber, Andreas Mayr +1
Not all real-world data are labeled, and when labels are not available, it is often costly to obtain them. Moreover, as many algorithms suffer from the curse of dimensionality, red…