1 citations · 3 across the 9 of their papers we have counts for
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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…