Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Purpose The primary aim of this study is to review the studies from different dimensions
including type of methods, experimentation setup and evaluation metrics used in the novel …

Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …

Abstract We introduce implicit Deep Adaptive Design (iDAD), a new method for performing
adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian …

Digital technologies are becoming increasingly prevalent in education, enabling
personalized, high quality education resources to be accessible by students across the …

Deep generative models often perform poorly in real-world applications due to the
heterogeneity of natural data sets. Heterogeneity arises from data containing different types …

Despite their successes, deep neural networks may make unreliable predictions when faced
with test data drawn from a distribution different to that of the training data, constituting a …

Many real-world situations allow for the acquisition of additional relevant information when
making an assessment with limited or uncertain data. However, traditional ML approaches …

Recent advances in foundation models, especially in large multi-modal models and
conversational agents, have ignited interest in the potential of generally capable embodied …

Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …