Lisa Jöckel
According to our database1,
Lisa Jöckel
authored at least 22 papers
between 2017 and 2023.
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Bibliography
2023
Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice.
CoRR, 2023
Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning?
Proceedings of the Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops, 2023
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning.
Proceedings of the Product-Focused Software Process Improvement, 2023
Reliability Estimation of ML for Image Perception: A Lightweight Nonlinear Transformation Approach Based on Full Reference Image Quality Metrics.
Proceedings of the 16th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, 2023
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning.
Proceedings of the 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2023
2022
Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components.
CoRR, 2022
Architectural Patterns for Handling Runtime Uncertainty of Data-Driven Models in Safety-Critical Perception.
Proceedings of the Computer Safety, Reliability, and Security, 2022
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models.
Proceedings of the Workshop on Artificial Intelligence Safety 2022 (SafeAI 2022) co-located with the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI2022), 2022
2021
Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts.
CoRR, 2021
Could We Relieve AI/ML Models of the Responsibility of Providing Dependable Uncertainty Estimates? A Study on Outside-Model Uncertainty Estimates.
Proceedings of the Computer Safety, Reliability, and Security, 2021
Towards a Common Testing Terminology for Software Engineering and Data Science Experts.
Proceedings of the Product-Focused Software Process Improvement, 2021
Using Complementary Risk Acceptance Criteria to Structure Assurance Cases for Safety-Critical AI Components.
Proceedings of the Workshop on Artificial Intelligence Safety 2021 co-located with the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021), 2021
Handling Uncertainties of Data-Driven Models in Compliance with Safety Constraints for Autonomous Behaviour.
Proceedings of the 17th European Dependable Computing Conference, 2021
Proceedings of the Model-Based Engineering of Collaborative Embedded Systems, 2021
2020
A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components.
Proceedings of the Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops, 2020
Requirements-Driven Method to Determine Quality Characteristics and Measurements for Machine Learning Software and Its Evaluation.
Proceedings of the 28th IEEE International Requirements Engineering Conference, 2020
Proceedings of the Quality of Information and Communications Technology, 2020
2019
Hardening of Artificial Neural Networks for Use in Safety-Critical Applications - A Mapping Study.
CoRR, 2019
Increasing Trust in Data-Driven Model Validation - A Framework for Probabilistic Augmentation of Images and Meta-data Generation Using Application Scope Characteristics.
Proceedings of the Computer Safety, Reliability, and Security, 2019
Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software.
Proceedings of the 19th IEEE International Conference on Software Quality, 2019
2017
Visualizing Probabilistic Multi-Phase Fluid Simulation Data using a Sampling Approach.
Comput. Graph. Forum, 2017