1 citations · 2 across the 12 of their papers we have counts for
13 papers
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…
Towards Evolutionary Optimization Using the Ising Model
Simon Klüttermann
In this paper, we study the problem of finding the global minima of a given function. Specifically, we consider complicated functions with numerous local minima, as is often the ca…
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…
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…
On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification
Simon Klüttermann, Jérôme Rutinowski, Anh Nguyen +3
In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset…