2023:

Explainable AI for time series classification and anomaly detection: Current state and open issues.
Keynote (invited talk) at Explainable AI for time series (XAI-TS) Workshop at ECML/PKDD 2023
Slides

Explainable AI: how far we have come and what’s left for us to do
Keynote (invited talk) at XKDD Workshop at ECML/PKDD 2023
Slides

Domain Transfer for Surface Defect Detection using Few-Shot Learning on Scarce Data.
Felix Gerschner, Jonas Paul, Lukas Schmid, Nico Barthel, Victor Gouromichos, Florian Schmid, Martin Atzmueller, Andreas Theissler (2023).
IEEE International Conference on Industrial Informatics (INDIN) 2023.

ROCKAD: Transferring ROCKET to whole time series anomaly detection.
Andreas Theissler, Manuel Wengert, Felix Gerschner (2023). In International Symposium on Intelligent Data Analysis 2023, Springer LNCS.

2022:

Comparing Human Haptic Perception and Robotic Force/Torque Sensing in a Simulated Surgical Palpation Task
Timo Markert, Sebastian Matich, Elias Hoerner, Jonas Pfanner, Andreas Theissler, Martin Atzmueller (2022),
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)

Journal paper:
Explainable AI for Time Series Classification: A review, taxonomy and research directions
Andreas Theissler, Francesco Spinnato, Udo Schlegel, Riccardo Guidotti (2022),
IEEE, IEEE Access
Open access: Link to paper

Journal paper:
XAI4EEG: Spectral and spatio-temporal explanation of Deep Learning-based Seizure Detection in EEG time series,
Dominik Raab, Andreas Theissler, Myra Spiliopoulou (2022),
Springer, Neural Computing
Open access: Link to paper

Journal paper:
VisGIL: Machine Learning based Visual Guidance for Interactive Labeling,
Benedikt Grimmeisen, Mohammad Chegini, Andreas Theissler (2022),
Springer, The Visual Computer
Open access: Link to paper

Visual Detection of Tiny Objects for Autonomous Robotic Pick-and-Place Operations,
Timo Markert, Sebastian Matich, Daniel Neykov, Markus Muenig, Andreas Theissler and Martin Atzmueller (2022),
IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2021).
(Best paper award: IES Young Professionals & Students Paper Award)
Link to paper

On Why the System Makes the Corner Case: AI-based Holistic Anomaly Detection for Autonomous Driving,
J. Pfeil, J. Wieland, T. Michalke, A. Theissler (2022),
IEEE Intelligent Vehicles Symposium (IV), 337-344
Link to paper

Journal paper:
ConfusionVis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices.
Andreas Theissler, Mark Thomas, Michael Burch, Felix Gerschner (2022),
Elsevier Knowledge-Based Systems, 2022, 108651, ISSN 0950-7051,
Open access: Link to paper

EduML: An explorative approach for students and lecturers in machine learning courses
Andreas Theissler, Philip Ritzer (2022),
Proceedings IEEE Educon 2022
Link to paper

State-of-the-art on writing a literature review: An overview of types and components
Alena Renner, Jenny Müller, Andreas Theissler (2022),
Proceedings IEEE Educon 2022
Link to paper

2021:

SPARROW: Semantically Coherent Prototypes for Image Classification.
Stefan Kraft, Klaus Brölemann, Andreas Theissler, Gjergji Kasneci (2021).
The British Machine Vision Conference (BMVC) 2021
Open Access: Link to paper

Journal paper:
Predictive Maintenance enabled by Machine Learning: Use cases and challenges in the automotive industry.
Andreas Theissler and Judith Perez-Velazquez and Marcel Kettelgerdes and Gordon Elger (2021).
Reliability Engineering & System Safety, ISSN 0951-8320, vol. 215,
Open Access: Link to paper

Fingertip 6-Axis Force/Torque Sensing for Texture Recognition in Robotic Manipulation.
Timo Markert, Sebastian Matich, Elias Hoerner, Andreas Theissler and Martin Atzmueller (2021).
Workshop Towards the factory of the future: advancements in planning and control of industrial robots.
IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2021).
(Best paper award: IES Young Professionals & Students Paper Award)
Link to paper, Video

Interpretable Machine Learning: A brief survey from the predictive maintenance perspective.
Vollert, Simon and Atzmueller, Martin and Theissler, Andreas (2021).
IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2021).
Link to paper

Challenges of machine learning-based RUL prognosis: A review on NASA’s C-MAPSS data set.
Vollert, Simon and Theissler, Andreas (2021).
IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2021).
Link to paper

2020:

The Machine Learning Model as a Guide: Pointing Users to Interesting Instances for Labeling through Visual Cues.
Grimmeisen, B. and Theissler, A. (2020). 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020).
ACM, ISBN: 978-1-4503-8750-7. Download , Link to paper

Cluster-Clean-Label: An interactive Machine Learning approach for labeling high-dimensional data.
Beil, D. and Theissler, A. (2020). 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020).
ACM, ISBN: 978-1-4503-8750-7. Download , Link to paper

XplainableClusterExplorer: A novel approach for Interactive Feature Selection for Clustering.
Fezer, E., Raab, D., Theissler, A. (2020). 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020).
ACM, ISBN: 978-1-4503-8750-7. Download , Link to paper

VIAL-AD: Visual Interactive Labelling for Anomaly Detection – An approach and open research questions.
Andreas Theissler, Anna-Lena Kraft, Max Rudeck, Fabian Erlenbusch (2020). Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020) pages 84-89, CEUR, Vol-2660, ISSN 1613-0073. Link to paper

ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classifiers.
Theissler A., Vollert S., Benz P., Meerhoff L.A., Fernandes M. (2020). In: Machine Learning and Knowledge Extraction. Proceedings CD-MAKE 2020. Lecture Notes in Computer Science, vol 12279. pages 281-300. ISBN: 978-3-030-57320-1, Springer, Cham. Download , Link to paper

Detecting potential subscribers on Twitch: A text mining approach with XGBoost Discovery challenge ChAT: CoolStoryBob.
Marvin Gärtner, Andreas Theissler, and Marc Fernandes (2020). Proceedings of ECML-PKDD 2020 ChAT Discovery Challenge on Chat Analytics for Twitch. CEUR, Vol-2661, ISSN 1613-0073. Link to paper

Evaluation of Machine Learning for Sensorless Detection and Classification of Faults in Electromechanical Drive Systems.
Grüner, T., Böllhoff, F., Meisetschläger, R., Vydrenko, A., Bator, M., Dicks, A., & Theissler, A. (2020). Proceedings 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Procedia Computer Science, Volume 176, pages 1586-1595, 2020, Elsevier. ISSN 1877-0509. Link to paper

A deep learning approach to prepare participants for negotiations by recognizing emotions with voice analysis.
Jonas Maier, Daniel Schlechte, Marc Fernandes, Andreas Theissler (2020). Local Proceedings International Conference on Group Decision and Negotiation 2020. Download

OCADaMi: One-Class Anomaly Detection and Data Mining Toolbox.
Theissler A., Frey S., Ehlert J. (2020). In: Machine Learning and Knowledge Discovery in Databases. Proceedings ECML PKDD 2019. Lecture Notes in Computer Science, vol 11908. pages 764-768. ISBN: 978-3-030-46132-4, Springer, Cham. Download preprint , Link to paper

before 2020:

Journal paper:
In Situ Failure Detection of Electronic Control Units Using Piezoresistive Stress Sensor.
Prisacaru, A., Palczynska, A., Theissler, A., Gromala, P., Han, B., & Zhang, G. Q. (2018).
IEEE Journal Transactions on Components, Packaging and Manufacturing Technology, 8(5), 750-763. Link to paper

Multi-class Novelty Detection in Diagnostic Trouble Codes from Repair Shops.
Theissler, A. (2017). Proceedings IEEE International Conference on Industrial Informatics. 2017. DOI: 10.1109/INDIN.2017.8104917. Download , Link to paper

Journal paper:
Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection.
Theissler, A. (2017). Elsevier Journal Knowledge-Based Systems. Vol 123, May 2017, 163-173. Download accepted manuscript , Link to paper

Anomaly detection in recordings from in-vehicle networks.
Theissler, A. (2014). Proceedings International Workshop on Big Data Applications and Principles 2014, Madrid. Download

Detecting anomalies in multivariate time series from automotive systems.
Theissler, A. (2013). Brunel University. PhD Thesis. ISBN: 9783843913485
Download

Interactive Anomaly Detection in time series resulting from local traffic measurements.
Theissler, A., Palmer, J. Rehborn, H., Dear, I. (2011).
Proceedings of the IADIS European Conference Data Mining 2011. IADIS Press, Lisbon.

Interactive knowledge discovery in recordings from vehicle tests.
Theissler, A., Ulmer, D., Dear, I. (2010).
Proceedings 33rd FISITA World Automotive Congress 2010. FISITA.
Download

PC-Based Measuring and Test System for High-Precision Recording and In-The-Loop-Simulation of Driver Assistance Functions.
Ulmer, D., Theissler, A., & Hünlich, K. (2010). Embedded World Conference 2010.