Colin Paterson
Cited by
Cited by
Assuring the machine learning lifecycle: Desiderata, methods, and challenges
R Ashmore, R Calinescu, C Paterson
arXiv preprint arXiv:1905.04223, 2019
A pattern for arguing the assurance of machine learning in medical diagnosis systems
C Picardi, R Hawkins, C Paterson, I Habli
International Conference on Computer Safety, Reliability, and Security, 165-179, 2019
FACT: A Probabilistic Model Checker for Formal Verification with Confidence Intervals.
R Calinescu, K Johnson, C Paterson
International Conference on Tools and Algorithms for the Construction and …, 2016
Self-adaptive role-based access control for business processes
CE da Silva, JDS da Silva, C Paterson, R Calinescu
2017 IEEE/ACM 12th International Symposium on Software Engineering for …, 2017
Socio-cyber-physical systems: models, opportunities, open challenges
R Calinescu, J Cámara, C Paterson
2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart …, 2019
Observation-enhanced QoS analysis of component-based systems
C Paterson, R Calinescu
IEEE Transactions on Software Engineering 46 (5), 526-548, 2018
Accurate analysis of quality properties of software with observation-based Markov chain refinement
C Paterson, R Calinescu
2017 IEEE International Conference on Software Architecture (ICSA), 121-130, 2017
Using runtime quantitative verification to provide assurance evidence for self-adaptive software
R Calinescu, S Gerasimou, K Johnson, C Paterson
Software Engineering for Self-Adaptive Systems III. Assurances, 223-248, 2017
Assurance argument patterns and processes for machine learning in safety-related systems
C Picardi, C Paterson, RD Hawkins, R Calinescu, I Habli
Proceedings of the Workshop on Artificial Intelligence Safety (SafeAI 2020 …, 2020
Assuring the safety of machine learning for pedestrian detection at crossings
L Gauerhof, R Hawkins, C Picardi, C Paterson, Y Hagiwara, I Habli
International Conference on Computer Safety, Reliability, and Security, 197-212, 2020
A fixed parameter optimal controller design for an active suspension system—A sensitivity analysis
RA Paterson, CA and Burnham, KJ and James, DJG and Williams
Mechatronics 4 (3), 317-329, 1994
Using unstructured data to improve the continuous planning of critical processes involving humans
C Paterson, R Calinescu, S Manandhar, D Wang
2019 IEEE/ACM 14th International Symposium on Software Engineering for …, 2019
Efficient parametric model checking using domain knowledge
R Calinescu, CA Paterson, K Johnson
IEEE Transactions on Software Engineering, 2019
Efficient parametric model checking using domain-specific modelling patterns
R Calinescu, K Johnson, C Paterson
2018 IEEE/ACM 40th International Conference on Software Engineering: New …, 2018
Detection and mitigation of rare subclasses in neural network classifiers
C Paterson, R Calinescu
arXiv preprint arXiv:1911.12780, 2019
Guidance on the assurance of machine learning in autonomous systems (AMLAS)
R Hawkins, C Paterson, C Picardi, Y Jia, R Calinescu, I Habli
arXiv preprint arXiv:2102.01564, 2021
Reinforcement learning with quantitative verification for assured multi-agent policies
J Riley, R Calinescu, C Paterson, D Kudenko, A Banks
13th International Conference on Agents and Artificial Intelligence, 2021
Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
D Weyns, B Schmerl, M Kishida, A Leva, M Litoiu, N Ozay, C Paterson, ...
arXiv preprint arXiv:2103.10847, 2021
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
C Paterson, R Calinescu, R Ashmore
ACM Computing Surveys, 2021
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers
C Paterson, H Wu, J Grese, R Calinescu, CS Pasareanu, C Barrett
arXiv preprint arXiv:2103.01629, 2021
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