activity
20182023
most citedAssuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

87 citations · 135 across the 7 of their papers we have counts for

collaborators

11 papers

cs.SE2023

Specification, Validation and Verification of Social, Legal, Ethical, Empathetic and Cultural Requirements for Autonomous Agents

Sinem Getir Yaman, Ana Cavalcanti, Radu Calinescu +3

Autonomous agents are increasingly being proposed for use in healthcare, assistive care, education, and other applications governed by complex human-centric norms. To ensure compli…

cs.SE2022

PRESTO: Predicting System-level Disruptions through Parametric Model Checking

Xinwei Fang, Radu Calinescu, Colin Paterson +1

Self-adaptive systems are expected to mitigate disruptions by continually adjusting their configuration and behaviour. This mitigation is often reactive. Typically, environmental o…

cs.SE2021

Quantitative Verification with Adaptive Uncertainty Reduction

Naif Alasmari, Radu Calinescu, Colin Paterson +1

Stochastic models are widely used to verify whether systems satisfy their reliability, performance and other nonfunctional requirements. However, the validity of the verification d…

cs.RO2021

Verified Synthesis of Optimal Safety Controllers for Human-Robot Collaboration

Mario Gleirscher, Radu Calinescu, James Douthwaite +5

We present a tool-supported approach for the synthesis, verification and validation of the control software responsible for the safety of the human-robot interaction in manufacturi…

cs.SE2021★ 1 cited

Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

Danny Weyns, Bradley Schmerl, Masako Kishida +5

Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectura…

cs.LG2021

DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers

Colin Paterson, Haoze Wu, John Grese +3

We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze…