Hyperparameter optimization (HPO) is important to leverage the full potential of machine
learning (ML). In practice, users are often interested in multi-objective (MO) problems, ie …

In consumer theory, ranking available objects by means of preference relations yields the
most common description of individual choices. However, preference-based models assume …

We consider a preference learning setting where every participant chooses an ordered list of
$ k $ most preferred items among a displayed set of candidates.(The set can be different for …

Individuals are constantly making choices---purchasing products, consuming Web content,
making social connections---so understanding what contributes to these decisions is crucial …

Providing users with alternatives to choose from is an essential component of many online
platforms, making the accurate prediction of choice vital to their success. A renewed interest …

Preference modelling lies at the intersection of economics, decision theory, machine
learning and statistics. By understanding individuals' preferences and how they make …

Discrete-choice models, such as Multinomial Logit, Probit, or Mixed-Logit, are widely used in
Marketing, Economics, and Operations Research: given a set of alternatives, the customer is …

The random utility maximization model is by far the most adopted framework to estimate
consumer choice behavior. However, behavioral economics has provided strong empirical …

We consider the problem of learning to choose from a given set of objects, where each
object is represented by a feature vector. Traditional approaches in choice modelling are …

The way that people make choices or exhibit preferences can be strongly affected by the set
of available alternatives, often called the choice set. Furthermore, there are usually …