Lisa Jöckel

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 26

Top Topics

Multiple Regression

Data Generation

Dempster Shafer Evidence Theory

Autonomous Vehicles

Top Venues

SAFECOMP Workshops

Publications

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)
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)
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)
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)
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 (2023)
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 (2023)
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 (2023)
Pascal Gerber, Lisa Jöckel, Michael Kläs
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. CoRR (2022)
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)
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 (2022)
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) (2022)
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 (2022)
Pascal Gerber, Lisa Jöckel, Michael Kläs
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. SafeAI@AAAI (2022)
Lisa Jöckel, Thomas Bauer, Michael Kläs, Marc P. Hauer, Janek Groß
Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts. CoRR (2021)
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)
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)
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)
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)
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 (2020)
Michael Kläs, Lisa Jöckel
A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components. SAFECOMP Workshops (2020)
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)
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)
Lisa Jöckel, Michael Kläs, Silverio Martínez-Fernández
Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software. QRS Companion (2019)
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)
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 (2019)
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) (2017)