Daniel Bethell
YOU?
Author Swipe
Guided Uncertainty Learning Using a Post-Hoc Evidential Meta-Model Open
Reliable uncertainty quantification remains a major obstacle to the deployment of deep learning models under distributional shift. Existing post-hoc approaches that retrofit pretrained models either inherit misplaced confidence or merely r…
Quantifying Adversarial Uncertainty in Evidential Deep Learning using Conflict Resolution Open
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. Evidential Deep Learning, an efficient paradigm for uncertainty q…
View article: Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding Open
Empowering safe exploration of reinforcement learning (RL) agents during training is a critical challenge towards their deployment in many real-world scenarios. When prior knowledge of the domain or task is unavailable, training RL agents …
Learning Fairer Representations with FairVIC Open
Mitigating bias in automated decision-making systems, particularly in deep learning models, is a critical challenge due to nuanced definitions of fairness, dataset-specific biases, and the inherent trade-off between fairness and accuracy. …
View article: Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction Open
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. Uncertainty quantification (UQ) methods estimate the model’…
Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction Open
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. Uncertainty quantification (UQ) methods estimate the model'…