1 citations · 2 across the 12 of their papers we have counts for
7 papers · 1 filter
From Data to Program: Fast & Direct Generative Program Inference from Empirical Data
Simon Klüttermann, Xueying Ding, Leman Akoglu
Estimating probability densities from a finite set of samples typically requires dataset-specific model fitting. We introduce PRODiGI, a pretrained data-to-program model that infer…
Why Ranking Anomaly Detection Algorithms Isn't as Reliable as You May Think
Simon Klüttermann, Jérôme Rutinowski, Frederik Polachowski +1
Anomaly detection is a safety-critical machine learning problem with applications ranging from fraud detection to network intrusion prevention and industrial monitoring. Despite th…
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…
MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection
Xueying Ding, Simon Klüttermann, Haomin Wen +2
Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological choices. Outlier detection (OD) o…