While interest in the application of machine learning to improve healthcare has grown
tremendously in recent years, a number of barriers prevent deployment in medical practice …

Recommender systems are decision support systems that help users to identify items of
relevance from a potentially large set of alternatives. In contrast to the mainstream …

In the past few years, the area of Machine Learning (ML) has witnessed tremendous
advancements, becoming a pervasive technology in a wide range of applications. One area …

An overarching goal of Artificial Intelligence (AI) is creating autonomous, social agents that
help people. Two important challenges, though, are that different people prefer different …

We propose Nester, a method for injecting neural networks into constrained structured
predictors. The job of the neural network (s) is to compute an initial, raw prediction that is …

Automated persuasion systems (APS) aim to persuade a user to believe something by
entering into a dialogue in which arguments and counterarguments are exchanged. To …

The goal of this paper is to investigate a decision support system for vehicle routing, where
the routing engine learns from the subjective decisions that human planners have made in …

Computational preference elicitation methods are tools used to learn people's preferences
quantitatively in a given context. Recent works on preference elicitation advocate for active …

We propose a new recommendation system for service and product bundling in the domain
of telecommunication and multimedia. Using this system, users can easily generate a …

Constraint acquisition can assist non-expert users to model their problems as constraint
networks. In active constraint acquisition, this is achieved through an interaction between the …