We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural
networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging …

We develop a general framework for social choice problems in which a limited number of
alternatives can be recommended to an agent population. In our budgeted social choice …

Learning preference distributions is a key problem in many areas (eg, recommender
systems, IR, social choice). However, many existing methods require restrictive data models …

While voting schemes provide an effective means for aggregating preferences, methods for
the effective elicitation of voter preferences have received little attention. We address this …

Learning preference distributions is a critical problem in many areas (eg, recommender
systems, IR, social choice). However, many existing learning and inference methods impose …

Ordered lists of objects are widely used as representational forms. Such ordered objects
include Web search results or bestseller lists. In spite of their importance, methods of …

Mixture models are versatile tools that are used extensively in many fields, including
operations, marketing, and econometrics. The main challenge in estimating mixture models …

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 …

Multi-winner social choice considers the problem of selecting a slate of K options to realize
some social objective. It has found application in the construction of political legislatures and …

The problem of learning conditional preference networks (CP-nets) from a set of examples
has received great attention recently. However, because of the randomicity of the users' …