3 citations · 14 across the 23 of their papers we have counts for
29 papers
Repurposing Obsolete Representations for Post-Deployment Adaptation
Daniel Bethell, Charmaine Barker, Simos Gerasimou
Deep neural networks are increasingly deployed in long-lived systems, where task requirements may change after training. In such settings, part of the original output space may bec…
Localisation-Aware Uncertainty for Pretrained Object Detection
Charmaine Barker, Daniel Bethell, Simos Gerasimou
Reliable uncertainty estimation is essential for deploying object detectors when distribution/covariate shift and adversarial attacks may occur. Existing approaches often require d…
Robust Evidential Learning Through Latent Consistency
Charmaine Barker, Daniel Bethell, Simos Gerasimou
Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial inputs can induce confident b…
Leveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verification
Simos Gerasimou, Xingyu Zhao
Combining Bayesian learning and quantitative verification is a powerful toolset for analysing key quantitative properties of software systems, like reliability and response time. H…
Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs
Gricel Vázquez, Alexandros Evangelidis, Sepeedeh Shahbeigi +2
Integrating Large Language Models (LLMs) into complex software systems enables the generation of human-understandable explanations of opaque AI processes, such as automated task pl…
Formally Guaranteed Control Adaptation for ODD-Resilient Autonomous Systems
Gricel Vázquez, Calum Imrie, Sepeedeh Shahbeigi +5
Ensuring reliable performance in situations outside the Operational Design Domain (ODD) remains a primary challenge in devising resilient autonomous systems. We explore this challe…
Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows
Adriel Sosa Marco, John Daniel Kirwan, Alexia Toumpa +1
Quantifying uncertainty in deep regression models is important both for understanding the confidence of the model and for safe decision-making in high-risk domains. Existing approa…
Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning
Daniel Bethell, Simos Gerasimou, Radu Calinescu +1
Reliable navigation in safety-critical environments requires both accurate hazard perception and principled uncertainty handling to strengthen downstream safety handling. Despite t…
Safe But Not Sorry: Reducing Over-Conservatism in Safety Critics via Uncertainty-Aware Modulation
Daniel Bethell, Simos Gerasimou, Radu Calinescu +1
Ensuring the safe exploration of reinforcement learning (RL) agents is critical for deployment in real-world systems. Yet existing approaches struggle to strike the right balance:…
Guided Uncertainty Learning Using a Post-Hoc Evidential Meta-Model
Charmaine Barker, Daniel Bethell, Simos Gerasimou
Reliable uncertainty quantification remains a major obstacle to the deployment of deep learning models under distributional shift. Existing post-hoc approaches that retrofit pretra…
Accelerating Policy Synthesis in Large-Scale MDPs via Hierarchical Adaptive Refinement
Alexandros Evangelidis, Gricel Vázquez, Simos Gerasimou
Software-intensive systems, such as software product lines and robotics, utilise Markov decision processes (MDPs) to capture uncertainty and analyse sequential decision-making prob…
Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
Charmaine Barker, Daniel Bethell, Simos Gerasimou
Reliability of deep learning models is critical for deployment in high-stakes applications, where out-of-distribution or adversarial inputs may lead to detrimental outcomes. Eviden…
Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning
Gricel Vázquez, Alexandros Evangelidis, Sepeedeh Shahbeigi +1
Producing robust task plans in human-robot collaborative missions is a critical activity in order to increase the likelihood of these missions completing successfully. Despite the…
Verification and External Parameter Inference for Stochastic World Models
Radu Calinescu, Sinem Getir Yaman, Simos Gerasimou +2
Given its ability to analyse stochastic models ranging from discrete and continuous-time Markov chains to Markov decision processes and stochastic games, probabilistic model checki…
Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
Daniel Bethell, Simos Gerasimou, Radu Calinescu +1
Empowering safe exploration of reinforcement learning (RL) agents during training is a critical challenge towards their deployment in many real-world scenarios. When prior knowledg…
Tree-Based versus Hybrid Graphical-Textual Model Editors: An Empirical Study of Testing Specifications
Ionut Predoaia, James Harbin, Simos Gerasimou +3
Tree-based model editors and hybrid graphical-textual model editors have advantages and limitations when editing domain models. Data is displayed hierarchically in tree-based model…
DeepKnowledge: Generalisation-Driven Deep Learning Testing
Sondess Missaoui, Simos Gerasimou, Nikolaos Matragkas
Despite their unprecedented success, DNNs are notoriously fragile to small shifts in data distribution, demanding effective testing techniques that can assess their dependability.…
Quantitative Assurance and Synthesis of Controllers from Activity Diagrams
Kangfeng Ye, Fang Yan, Simos Gerasimou
Probabilistic model checking is a widely used formal verification technique to automatically verify qualitative and quantitative properties for probabilistic models. However, captu…
Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
Daniel Bethell, Simos Gerasimou, Radu Calinescu
Deploying deep learning models in safety-critical applications remains a very challenging task, mandating the provision of assurances for the dependable operation of these models.…
Bayesian Learning for the Robust Verification of Autonomous Robots
Xingyu Zhao, Simos Gerasimou, Radu Calinescu +3
Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify the…
Software Performability Analysis Using Fast Parametric Model Checking
Xinwei Fang, Radu Calinescu, Simos Gerasimou +1
We present an efficient parametric model checking (PMC) technique for the analysis of software performability, i.e., of the performance and dependability properties of software sys…
Fast Parametric Model Checking through Model Fragmentation
Xinwei Fang, Radu Calinescu, Simos Gerasimou +1
Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the sys…
Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning
Mirko D'Angelo, Sona Ghahremani, Simos Gerasimou +4
Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multi-dimensional and complex design space. Data-driven approaches for C…
Supporting Robotic Software Migration Using Static Analysis and Model-Driven Engineering
Sophie Wood, Nicholas Matragkas, Dimitris Kolovos +2
The wide use of robotic systems contributed to developing robotic software highly coupled to the hardware platform running the robotic system. Due to increased maintenance cost or…