The inception of large language models has helped advance the state-of-the-art on
numerous natural language tasks. This has also opened the door for the development of …

The generalization of predictive models that follow the standard risk minimization paradigm
of machine learning can be hindered by the presence of spurious correlations in the data …

Existing federated learning models that follow the standard risk minimization paradigm of
machine learning often fail to generalize in the presence of spurious correlations in the …

As machine and deep learning models are increasingly leveraged in predictive process
monitoring, the focus has shifted towards making these models explainable. The successful …

In recent years, many predictive models have been successfully applied to predictive
process monitoring, enabling tasks such as predicting the next activity, remaining time, or …

Predictive monitoring is a subfield of process mining that aims to predict how a running case
will unfold in the future. One of its main challenges is forecasting the sequence of activities …

Dialogue State Tracking (DST), a key component of task-oriented conversation systems,
represents user intentions by determining the values of pre-defined slots in an ongoing …

In predictive process monitoring, predictive models are vulnerable to adversarial attacks,
where input perturbations can lead to incorrect predictions. Unlike in computer vision, where …

The inception of large language models has helped advance the state-of-the-art on
numerous natural language tasks. This has also opened the door for the development of …

Process simulation is an analysis tool in process mining that allows users to measure the
impact of changes, prevent losses, and update the process without risks or costs. In the …