1 citations · 3 across the 9 of their papers we have counts for
9 papers
RangeAD: Fast On-Model Anomaly Detection
Luca Hinkamp, Simon Klüttermann, Emmanuel Müller
In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts. Typically, this is implemented by running a separate…
Unsupervised Symbolic Anomaly Detection
Md Maruf Hossain, Tim Katzke, Simon Klüttermann +1
We propose SYRAN, an unsupervised anomaly detection method based on symbolic regression. Instead of encoding normal patterns in an opaque, high-dimensional model, our method learns…
FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models
Simon Klüttermann, Tim Katzke, Phuong Huong Nguyen +1
Tabular foundation models, specifically Prior-Data Fitted Networks (PFNs), have revolutionized outlier detection (OD) by enabling unsupervised zero-shot adaptation to new datasets…
Towards Foundation Models for Consensus Rank Aggregation
Yijun Jin, Simon Klüttermann, Chiara Balestra +1
Aggregating a consensus ranking from multiple input rankings is a fundamental problem with applications in recommendation systems, search engines, job recruitment, and elections. D…
Rare anomalies require large datasets: About proving the existence of anomalies
Simon Klüttermann, Emmanuel Müller
Detecting whether any anomalies exist within a dataset is crucial for effective anomaly detection, yet it remains surprisingly underexplored in anomaly detection literature. This p…
Polyra Swarms: A Shape-Based Approach to Machine Learning
Simon Klüttermann, Emmanuel Müller
We propose Polyra Swarms, a novel machine-learning approach that approximates shapes instead of functions. Our method enables general-purpose learning with very low bias. In partic…
Unsupervised Surrogate Anomaly Detection
Simon Klüttermann, Tim Katzke, Emmanuel Müller
In this paper, we study unsupervised anomaly detection algorithms that learn a neural network representation, i.e. regular patterns of normal data, which anomalies are deviating fr…
A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany
Ina Dormuth, Sven Franke, Marlies Hafer +6
In this study, we examine the reliability of AI-based Voting Advice Applications (VAAs) and large language models (LLMs) in providing objective political information. Our analysis…
Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics
Minjae Ok, Simon Klüttermann, Emmanuel Müller
Anomaly detection is a dynamic field, in which the evaluation of models plays a critical role in understanding their effectiveness. The selection and interpretation of the evaluati…