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Lisa Jöckel
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2020 – today
- 2023
- [c15]Janek Groß, Michael Kläs, Lisa Jöckel, Pascal Gerber:
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. DSN-W 2023: 231-238 - [c14]João-Vitor Zacchi, Francesco Carella, Priyank Upadhya, Shanza Ali Zafar, John Molloy, Lisa Jöckel, Janek Groß, Núria Mata, Nguyen Anh Vu Doan:
Reliability Estimation of ML for Image Perception: A Lightweight Nonlinear Transformation Approach Based on Full Reference Image Quality Metrics. MCSoC 2023: 186-193 - [c13]Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber, Markus Scholz, Jonathan Eberle, Marc Teschner, Daniel Seifert, Richard Hawkins, John Molloy, Jens Ottnad:
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. PROFES (1) 2023: 151-158 - [c12]Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber:
Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning? SAFECOMP Workshops 2023: 314-327 - [i9]Janek Groß, Michael Kläs, Lisa Jöckel, Pascal Gerber:
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. CoRR abs/2305.14872 (2023) - [i8]Lisa Jöckel, Michael Kläs, Georg Popp, Nadja Hilger, Stephan Fricke:
Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice. CoRR abs/2311.05245 (2023) - [i7]Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber, Markus Scholz, Jonathan Eberle, Marc Teschner, Daniel Seifert, Richard Hawkins, John Molloy, Jens Ottnad:
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. CoRR abs/2312.04917 (2023) - 2022
- [j2]Julien Siebert, Lisa Jöckel, Jens Heidrich, Adam Trendowicz, Koji Nakamichi, Kyoko Ohashi, Isao Namba, Rieko Yamamoto, Mikio Aoyama:
Construction of a quality model for machine learning systems. Softw. Qual. J. 30(2): 307-335 (2022) - [c11]Pascal Gerber, Lisa Jöckel, Michael Kläs:
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. SafeAI@AAAI 2022 - [c10]Janek Groß, Rasmus Adler, Michael Kläs, Jan Reich, Lisa Jöckel, Roman Gansch:
Architectural Patterns for Handling Runtime Uncertainty of Data-Driven Models in Safety-Critical Perception. SAFECOMP 2022: 284-297 - [i6]Pascal Gerber, Lisa Jöckel, Michael Kläs:
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. CoRR abs/2201.03263 (2022) - [i5]Michael Kläs, Lisa Jöckel, Rasmus Adler, Jan Reich:
Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components. CoRR abs/2202.05313 (2022) - [i4]Janek Groß, Rasmus Adler, Michael Kläs, Jan Reich, Lisa Jöckel, Roman Gansch:
Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception. CoRR abs/2206.06838 (2022) - 2021
- [c9]Michael Kläs, Rasmus Adler, Ioannis Sorokos, Lisa Jöckel, Jan Reich:
Handling Uncertainties of Data-Driven Models in Compliance with Safety Constraints for Autonomous Behaviour. EDCC 2021: 95-102 - [c8]Michael Klaes, Rasmus Adler, Lisa Jöckel, Janek Groß, Jan Reich:
Using Complementary Risk Acceptance Criteria to Structure Assurance Cases for Safety-Critical AI Components. AISafety@IJCAI 2021 - [c7]Lisa Jöckel, Thomas Bauer, Michael Kläs, Marc P. Hauer, Janek Groß:
Towards a Common Testing Terminology for Software Engineering and Data Science Experts. PROFES 2021: 281-289 - [c6]Lisa Jöckel, Michael Kläs:
Could We Relieve AI/ML Models of the Responsibility of Providing Dependable Uncertainty Estimates? A Study on Outside-Model Uncertainty Estimates. SAFECOMP 2021: 18-33 - [p1]Torsten Bandyszak, Lisa Jöckel, Michael Kläs, Sebastian Törsleff, Thorsten Weyer, Boris Wirtz:
Handling Uncertainty in Collaborative Embedded Systems Engineering. Model-Based Engineering of Collaborative Embedded Systems 2021: 147-170 - [i3]Lisa Jöckel, Thomas Bauer, Michael Kläs, Marc P. Hauer, Janek Groß:
Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts. CoRR abs/2108.13837 (2021) - 2020
- [c5]Julien Siebert, Lisa Jöckel, Jens Heidrich, Koji Nakamichi, Kyoko Ohashi, Isao Namba, Rieko Yamamoto, Mikio Aoyama:
Towards Guidelines for Assessing Qualities of Machine Learning Systems. QUATIC 2020: 17-31 - [c4]Koji Nakamichi, Kyoko Ohashi, Isao Namba, Rieko Yamamoto, Mikio Aoyama, Lisa Jöckel, Julien Siebert, Jens Heidrich:
Requirements-Driven Method to Determine Quality Characteristics and Measurements for Machine Learning Software and Its Evaluation. RE 2020: 260-270 - [c3]Michael Kläs, Lisa Jöckel:
A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components. SAFECOMP Workshops 2020: 315-327 - [i2]Julien Siebert, Lisa Jöckel, Jens Heidrich, Koji Nakamichi, Kyoko Ohashi, Isao Namba, Rieko Yamamoto, Mikio Aoyama:
Towards Guidelines for Assessing Qualities of Machine Learning Systems. CoRR abs/2008.11007 (2020)
2010 – 2019
- 2019
- [c2]Lisa Jöckel, Michael Kläs, Silverio Martínez-Fernández:
Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software. QRS Companion 2019: 540-541 - [c1]Lisa Jöckel, Michael Kläs:
Increasing Trust in Data-Driven Model Validation - A Framework for Probabilistic Augmentation of Images and Meta-data Generation Using Application Scope Characteristics. SAFECOMP 2019: 155-164 - [i1]Rasmus Adler, Mohammed Naveed Akram, Pascal Bauer, Patrik Feth, Pascal Gerber, Andreas Jedlitschka, Lisa Jöckel, Michael Kläs, Daniel Schneider:
Hardening of Artificial Neural Networks for Use in Safety-Critical Applications - A Mapping Study. CoRR abs/1909.03036 (2019) - 2017
- [j1]Mathias Hummel, Lisa Jöckel, J. Schäfer, Mark W. Hlawitschka, Christoph Garth:
Visualizing Probabilistic Multi-Phase Fluid Simulation Data using a Sampling Approach. Comput. Graph. Forum 36(3): 469-477 (2017)
Coauthor Index
aka: Michael Klaes
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