Advanced model‐based controllers are well established in process industries. However,
such controllers require regular maintenance to maintain acceptable performance. It is a …

We develop reinforcement learning trials for discovering individualized treatment regimens
for life‐threatening diseases such as cancer. A temporal‐difference learning method called …

The main objective in the ad recommendation problem is to find a strategy that, for each
visitor of the website, selects the ad that has the highest probability of being clicked. This …

In many marketing applications, companies use technology for interacting with their
customers and making product or services recommendations. Today, these marketing …

Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of
graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher …

The problem of optimally managing the collections process by taxation authorities is one of
prime importance, not only for the revenue it brings but also as a means to administer a fair …

Predictive process monitoring is a family of techniques to analyze events produced during
the execution of a business process in order to predict the future state or the final outcome of …

Most data mining algorithms and tools stop at discovered customer models, producing
distribution information on customer profiles. Such techniques, when applied to industrial …

Successful applications of reinforcement learning in real-world problems often require
dealing with partially observable states. It is in general very challenging to construct and …

Deciding who should receive a mail-order catalog is among the most important decisions
that mail-order-catalog firms must address. In practice, the current approach to the problem …