We develop a data-driven decision model to improve process quality in manufacturing. A
challenge for traditional methods in quality management is to handle high-dimensional and …

In many settings, a decision maker wishes to learn a rule, or policy, that maps from
observable characteristics of an individual to an action. Examples include selecting offers …

Problem definition: We study the adherence to the recommendations of a decision support
system (DSS) for clearance markdowns at Zara, the Spanish fast fashion retailer. Our focus …

Predictive analytics is increasingly used to guide decision making in many applications.
However, in practice, we often have limited data on the true predictive task of interest and …

Data-driven pricing strategies are becoming increasingly common, where customers are
offered a personalized price based on features that are predictive of their valuation of a …

We study the problem of dynamic batch learning in high-dimensional sparse linear
contextual bandits, where a decision maker, under a given maximum-number-of-batch …

When quantitative models are used to support decision-making on complex and important
topics, understanding a model's``reasoning''can increase trust in its predictions, expose …

Optimal stopping is the problem of deciding when to stop a stochastic system to obtain the
greatest reward, arising in numerous application areas such as finance, healthcare, and …

Background Advanced machine learning models have received wide attention in assisting
medical decision making due to the greater accuracy they can achieve. However, their …

1. Executive summary Humanitarian predictive analytics is the use of big data to feed
machine learning and statistical models to calculate the probable characteristics of …