Simon Klüttermann

According to our database, Simon Klüttermann authored at least 26 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

RangeAD: Fast On-Model Anomaly Detection.

Luca Hinkamp, Simon Klüttermann, Emmanuel Müller

CoRR, March, 2026

Unsupervised Symbolic Anomaly Detection.

Md Maruf Hossain, Tim Katzke, Simon Klüttermann, Emmanuel Müller

CoRR, March, 2026

FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models.

Simon Klüttermann, Tim Katzke, Phuong Huong Nguyen, Emmanuel Müller

CoRR, March, 2026

Towards Foundation Models for Consensus Rank Aggregation.

Yijun Jin, Simon Klüttermann, Chiara Balestra, Emmanuel Müller

CoRR, March, 2026

2025

Towards Evolutionary Optimization Using the Ising Model.

Simon Klüttermann

CoRR, November, 2025

Rare anomalies require large datasets: About proving the existence of anomalies.

Simon Klüttermann, Emmanuel Müller

CoRR, August, 2025

We Need to Rethink Benchmarking in Anomaly Detection.

Philipp Röchner, Simon Klüttermann, Franz Rothlauf, Daniel Schlör

CoRR, July, 2025

Polyra Swarms: A Shape-Based Approach to Machine Learning.

Simon Klüttermann, Emmanuel Müller

CoRR, June, 2025

Abstract ensembles for anomaly detection and beyond.

Simon Klüttermann

PhD thesis, 2025

Unsupervised Surrogate Anomaly Detection.

Simon Klüttermann, Tim Katzke, Emmanuel Müller

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2025

Evaluating Anomaly Detection Algorithms: The Role of Hyperparameters and Standardized Benchmarks.

Simon Klüttermann, Shubham Gupta, Emmanuel Müller

Proceedings of the 12th IEEE International Conference on Data Science and Advanced Analytics, 2025

2024

Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics.

Minjae Ok, Simon Klüttermann, Emmanuel Müller

CoRR, 2024

About Test-time training for outlier detection.

Simon Klüttermann, Emmanuel Müller

CoRR, 2024

On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification.

Simon Klüttermann, Jérôme Rutinowski, Anh Nguyen, Britta Grimme, Moritz Roidl, Emmanuel Müller

CoRR, 2024

Benchmarking Trust: A Metric for Trustworthy Machine Learning.

Jérôme Rutinowski, Simon Klüttermann, Jan Endendyk, Christopher Reining, Emmanuel Müller

Proceedings of the Explainable Artificial Intelligence, 2024

On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values.

Simon Klüttermann, Chiara Balestra, Emmanuel Müller

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2024

Autoencoder Optimization for Anomaly Detection: A Comparative Study with Shallow Algorithms.

Vikas Kumar, Vishesh Srivastava, Sadia Mahjabin, Arindam Pal, Simon Klüttermann, Emmanuel Müller

Proceedings of the International Joint Conference on Neural Networks, 2024

The Phenomenon of Correlated Representations in Contrastive Learning.

Simon Klüttermann, Jérôme Rutinowski, Emmanuel Müller

Proceedings of the International Joint Conference on Neural Networks, 2024

Evaluating Anomaly Detection Algorithms: A Multi-Metric Analysis Across Variable Class Imbalances.

Mohammad-Sahadet Hossain, Mohammad Sakhawat Hossain, Simon Klüttermann, Emmanuel Müller

Proceedings of the International Joint Conference on Neural Networks, 2024

On the Effectiveness of Heterogeneous Ensemble Methods for Re-Identification.

Simon Klüttermann, Jérôme Rutinowski, Frederik Polachowski, Anh Nguyen, Britta Grimme, Moritz Roidl, Emmanuel Müller

Proceedings of the International Conference on Machine Learning and Applications, 2024

Towards Highly Efficient Anomaly Detection for Predictive Maintenance.

Simon Klüttermann, Vanlal Peka, Philipp Doebler, Emmanuel Müller

Proceedings of the International Conference on Machine Learning and Applications, 2024

A Laser-based Volumetric Measurement Approach for Industrial Settings.

Jérôme Rutinowski, Niklas Schrötler, Simon Klüttermann, Moritz Roidl, Christian Janiesch

Proceedings of the 20th IEEE International Conference on Automation Science and Engineering, 2024

2023

Evaluating and Comparing Heterogeneous Ensemble Methods for Unsupervised Anomaly Detection.

Simon Klüttermann, Emmanuel Müller

Proceedings of the International Joint Conference on Neural Networks, 2023

On Graph Representation based Re-Identification - A Proof of Concept.

Simon Klüttermann, Jérôme Rutinowski, Anh Nguyen, Christopher Reining, Moritz Roidl, Emmanuel Müller

Proceedings of the IEEE International Conference on Data Mining, 2023

2022

Towards Graph Representation based Re-Identification of Chipwood Pallet Blocks.

Simon Klüttermann, Jérôme Rutinowski, Christopher Reining, Moritz Roidl, Emmanuel Müller

Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

Post-Robustifying Deep Anomaly Detection Ensembles by Model Selection.

Benedikt Böing, Simon Klüttermann, Emmanuel Müller

Proceedings of the IEEE International Conference on Data Mining, 2022