4 accepted papers
A digital twin (DT) is a virtual model of a real-world system that can assist decision-making by simulating scenarios induced by different policies. However, the typical design process of machine learning-based DTs does not optimise for this objective. We prove that, when model capacity is limited, …
Neural networks (NNs) often have critical behavioural trade-offs that are set at design time with hyperparameters—such as reward weighting in reinforcement learning or quantile targets in regression. Post-deployment, however, user preferences can evolve, making initially optimal settings undesirabl…
Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions. In complex settings, the state and action variables, and available data and knowledge relevant to a system can constantly change, requiring digital twins to continuously update with thes…
Causal inference is essential for developing and evaluating medical interventions, yet real-world medical datasets are often difficult to access due to regulatory barriers. This makes synthetic data a potentially valuable asset that enables these medical analyses, along with the development of new i…