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ORCIDSimon Burton 0001
Person information
- affiliation: Fraunhofer Institute for Cognitive Systems (IKS), Munich, Germany
- affiliation: University of York, UK
Other persons with the same name
- Simon Burton — disambiguation page
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2020 – today
- 2024
[c28]Philipp Altmann, Julian Schönberger, Steffen Illium, Maximilian Zorn, Fabian Ritz, Tom Haider, Simon Burton, Thomas Gabor:
Emergence in Multi-agent Systems: A Safety Perspective. ISoLA (2) 2024: 104-120- [c27]Tom Haider
, Karsten Roscher, Benjamin Herd, Felippe Schmoeller Roza, Simon Burton:
Can you trust your Agent? The Effect of Out-of-Distribution Detection on the Safety of Reinforcement Learning Systems. SAC 2024: 1569-1578 - [c26]Benjamin Herd, Simon Burton:
Can you trust your ML metrics? Using Subjective Logic to determine the true contribution of ML metrics for safety. SAC 2024: 1579-1586 - [c25]Benjamin Herd, João-Vitor Zacchi, Simon Burton:
A Deductive Approach to Safety Assurance: Formalising Safety Contracts with Subjective Logic. SAFECOMP (Workshops) 2024: 213-226 - [c24]Simon Burton, Benjamin Herd, João-Vitor Zacchi:
Uncertainty-Aware Evaluation of Quantitative ML Safety Requirements. SAFECOMP (Workshops) 2024: 391-404 - [i8]Josh Hunter, John A. McDermid, Simon Burton:
Safety Analysis of Autonomous Railway Systems: An Introduction to the SACRED Methodology. CoRR abs/2403.12114 (2024) - [i7]Philipp Altmann, Julian Schönberger, Steffen Illium, Maximilian Zorn, Fabian Ritz, Tom Haider, Simon Burton, Thomas Gabor:
Emergence in Multi-Agent Systems: A Safety Perspective. CoRR abs/2408.04514 (2024) - 2023
- [c23]Dirk Eilers, Simon Burton, Felippe Schmoeller da Roza, Karsten Roscher:
Safety Assurance with Ensemble-based Uncertainty Estimation and overlapping alternative Predictions in Reinforcement Learning. SafeAI@AAAI 2023 - [c22]Iwo Kurzidem, Simon Burton, Philipp Schleiss:
AI for Safety: How to use Explainable Machine Learning Approaches for Safety Analyses. AISafety/SafeRL@IJCAI 2023 - [c21]Emmanouil Seferis, Simon Burton, Chih-Hong Cheng:
Statistical Property Testing for Generative Models. Tiny Papers @ ICLR 2023 - [c20]Emmanouil Seferis, Simon Burton, Chih-Hong Cheng:
Can Conformal Prediction Obtain Meaningful Safety Guarantees for ML Models? Tiny Papers @ ICLR 2023 - [c19]Iwo Kurzidem, Simon Burton, Philipp Schleiss:
SafeSens - Uncertainty Quantification of Complex Perception Systems. ITSC 2023: 5805-5810 - [c18]Chih-Hong Cheng, Venkatesh Prasad Venkataramanan, Pragya Kirti Gupta, Yun-Fei Hsu, Simon Burton:
Safeguarding Learning-based Control for Smart Energy Systems with Sampling Specifications. PRDC 2023: 239-241 - [i6]Chih-Hong Cheng, Venkatesh Prasad Venkataramanan, Pragya Kirti Gupta, Yun-Fei Hsu, Simon Burton:
Safeguarding Learning-based Control for Smart Energy Systems with Sampling Specifications. CoRR abs/2308.06069 (2023) - 2022
- [c17]Iwo Kurzidem, Adam Misik, Philipp Schleiss, Simon Burton:
Safety Assessment: From Black-Box to White-Box. ISSRE Workshops 2022: 295-300 - [c16]Carmen Cârlan, Lydia Gauerhof, Barbara Gallina, Simon Burton:
Automating Safety Argument Change Impact Analysis for Machine Learning Components. PRDC 2022: 43-53 - [c15]Tobias Schuster, Emmanouil Seferis, Simon Burton, Chih-Hong Cheng:
Formally Compensating Performance Limitations for Imprecise 2D Object Detection. SAFECOMP 2022: 269-283 - [c14]Chih-Hong Cheng, Tobias Schuster, Simon Burton:
Logically Sound Arguments for the Effectiveness of ML Safety Measures. SAFECOMP Workshops 2022: 343-350 - [i5]Simon Burton:
A causal model of safety assurance for machine learning. CoRR abs/2201.05451 (2022) - [i4]Harald Rueß, Simon Burton:
Safe AI - How is this Possible? CoRR abs/2201.10436 (2022) - [i3]Tobias Schuster, Emmanouil Seferis, Simon Burton, Chih-Hong Cheng:
Unaligned but Safe - Formally Compensating Performance Limitations for Imprecise 2D Object Detection. CoRR abs/2202.05123 (2022) - [i2]Chih-Hong Cheng, Nguyen Anh Vu Doan, Balahari Balu, Franziska Schwaiger, Emmanouil Seferis, Simon Burton, Yassine Qamsane, Ankit Shukla, Yinchong Yang, Zhiliang Wu, Andreas Hapfelmeier, Ingo Thon:
Facilitating Change Implementation for Continuous ML-Safety Assurance. CoRR abs/2209.11632 (2022) - 2021
- [j2]Simon Burton, John A. McDermid, Philip Garnett, Rob Weaver:
Safety, Complexity, and Automated Driving: Holistic Perspectives on Safety Assurance. Computer 54(8): 22-32 (2021) - [c13]Wanli Chang, Shuai Zhao, Simon Burton, Haitong Wang, Ting Chen, Nan Chen, Neil C. Audsley:
Invited: Hardware/Software Co-Synthesis and Co-Optimization for Autonomous Systems. DAC 2021: 1319-1322 - [c12]Marta Grobelna, João-Vitor Zacchi, Philipp Schleiß, Simon Burton:
Dynamic Risk Management for Safely Automating Connected Driving Maneuvers. EDCC 2021: 9-16 - [c11]Simon Burton, Iwo Kurzidem, Adrian Schwaiger, Philipp Schleiss, Michael Unterreiner, Torben Gräber, Philipp Becker:
Safety Assurance of Machine Learning for Chassis Control Functions. SAFECOMP 2021: 149-162 - [e1]Rasmus Adler, Amel Bennaceur, Simon Burton, Amleto Di Salle, Nicola Nostro, Rasmus Løvenstein Olsen, Selma Saidi, Philipp Schleiss, Daniel Schneider, Hans-Peter Schwefel:
Dependable Computing - EDCC 2021 Workshops - DREAMS, DSOGRI, SERENE 2021, Munich, Germany, September 13, 2021, Proceedings. Communications in Computer and Information Science 1462, Springer 2021, ISBN 978-3-030-86506-1 [contents] - [i1]Chih-Hong Cheng, Tobias Schuster, Simon Burton:
Logically Sound Arguments for the Effectiveness of ML Safety Measures. CoRR abs/2111.02649 (2021) - 2020
- [j1]Simon Burton, Ibrahim Habli, Tom Lawton, John A. McDermid, Phillip Morgan, Zoë Porter:
Mind the gaps: Assuring the safety of autonomous systems from an engineering, ethical, and legal perspective. Artif. Intell. 279 (2020)
2010 – 2019
- 2019
- [c10]Simon Burton, Lydia Gauerhof, Bibhuti Bhusan Sethy, Ibrahim Habli, Richard Hawkins:
Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions. SAFECOMP Workshops 2019: 365-377 - 2018
- [c9]Peter Munk, Andreas Abele, Eike Thaden, Arne Nordmann, Rakshith Amarnath, Markus Schweizer, Simon Burton:
Semi-automatic safety analysis and optimization. DAC 2018: 29:1-29:6 - [c8]Lydia Gauerhof, Peter Munk, Simon Burton:
Structuring Validation Targets of a Machine Learning Function Applied to Automated Driving. SAFECOMP 2018: 45-58 - [c7]John MacGregor, Simon Burton:
Challenges in Assuring Highly Complex, High Volume Safety-Critical Software. SAFECOMP Workshops 2018: 252-264 - 2017
- [c6]Simon Burton, Lydia Gauerhof, Christian Heinzemann:
Making the Case for Safety of Machine Learning in Highly Automated Driving. SAFECOMP Workshops 2017: 5-16 - 2016
- [c5]Rakshith Amarnath, Peter Munk, Eike Thaden, Arne Nordmann, Simon Burton:
Dependability Challenges in the Model-Driven Engineering of Automotive Systems. ISSRE Workshops 2016: 1-4 - 2012
- [c4]Simon Burton, Jürgen Likkei, Priyamvadha Vembar, Marko Wolf:
Automotive functional safety = safety + security. SECURIT 2012: 150-159
2000 – 2009
- 2005
- [c3]Paul Baker, Paul Bristow, Clive Jervis, David J. King, Robert Thomson, Bill Mitchell, Simon Burton:
Detecting and resolving semantic pathologies in UML sequence diagrams. ESEC/SIGSOFT FSE 2005: 50-59 - 2002
- [b1]Simon Burton:
Automated generation of high-integrity test suites from graphical specifications. University of York, UK, 2002 - 2001
- [c2]Karen Allenby, Simon Burton, Darren Lee Buttle, John A. McDermid, John Murdoch, Alan Stephenson, Mike Bardill, Stuart Hutchesson:
A Family-Oriented Software Development Process for Engine Controllers. PROFES 2001: 210-226
1990 – 1999
- 1998
- [c1]John A. McDermid, Andy Galloway, Simon Burton, John A. Clark, Ian Toyn, Nigel J. Tracey, Samuel H. Valentine:
Towards Industrially Applicable Formal Methods: Three Small Steps and One Giant Leap. ICFEM 1998: 76-89
Coauthor Index
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