3 citations · 14 across the 23 of their papers we have counts for
8 papers · 1 filter
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…