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

In this critical survey, we analyze typical claims on the relationship between explainable AI
(XAI) and fairness to disentangle the multidimensional relationship between these two …

Bayesian neural networks (BNNs) have recently gained popularity due to their ability to
quantify model uncertainty. However, specifying a prior for BNNs that captures relevant …

When applied to critical domains, machine learning models usually need to comply with
prior knowledge and domain-specific requirements. For example, one may require that a …

Abstract Neural Networks (NNs) have been widely used in supervised learning due to their
ability to model complex nonlinear patterns, often presented in high-dimensional data such …

Devoted to interpreting the explicit behaviors of machine learning models, explanation
methods can identify implicit characteristics of models to improve trustworthiness. However …

Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-
box functions, but there are many domains where the function is not completely a black-box …

In this critical survey, we analyze typical claims on the relationship between explainable AI
(XAI) and fairness to disentangle the multidimensional relationship between these two …

We develop a novel method for carrying out model selection for Bayesian autoencoders
(BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of …

Using artificial intelligence (AI) systems for informing high-stakes decisions has become
increasingly pervasive in a variety of domains, including but not limited to hiring, lending, or …