We study preferential Bayesian optimization (BO) where reliable feedback is limited to
pairwise comparison called duels. An important challenge in preferential BO, which uses the …

Typical black-box optimization approaches in robotics focus on learning from metric scores.
However, that is not always possible, as not all developers have ground truth available …

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

At its core, this thesis aims to enhance the practicality of deep learning by improving the
label and training efficiency of deep learning models. To this end, we investigate data subset …

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

This paper investigates the adaptation of control policies for personalized dose delivery in
plasma medicine using preference-learning based Bayesian optimization. Preference …

Human-in-the-loop optimization utilizes human expertise to guide machine optimizers
iteratively and search for an optimal solution in a solution space. While prior empirical …

Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker's
latent preferences over available design choices. While preferences often involve multiple …

This research introduces a novel approach to optimizing control policies for waste cranes
operating at waste-to-energy plants. Although previous methods forced people to define …

Preferential Bayesian optimization (PBO) is a framework for human-in-the-loop optimization
to maximize black-box human preference functions such as seeking perceptually good …