The use of Shapley Values (SVs) to explain machine learning model predictions is
established. Recent research efforts have been devoted to generating efficient Neural …

In diverse fields of application, Bayesian Optimization (BO) has been proposed to find the
optima of black-box functions, surpassing human-performed searches. BO's appeal lies in its …

The field of explainable artificial intelligence (XAI) attempts to develop methods that provide
insight into how complicated machine learning methods make predictions. Many methods of …

In order to trust the predictions of a machine learning algorithm, it is necessary to understand
the factors that contribute to those predictions. In the case of probabilistic and uncertainty …

We investigate algorithmic decision problems where agents can respond strategically to the
decision maker's (DM) models. The demand for clear and actionable explanations from DMs …

Tree models have made an impressive progress during the past years, while an important
problem is to understand how these models predict, in particular for critical applications such …

In healthcare, thanks to many model agnostic methods, explainability of the prediction
scores made by deep learning applications has improved. However, we note that for daily or …

With the proliferation of screening tools for chemical testing, it is now possible to create vast
databases of chemicals easily. However, rigorous statistical methodologies employed to …