AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …

This editorial of the special issue “Representing, Processing, and Learning Preferences:
Theoretical and Practical Challenges” surveys past and ongoing research on preferences in …

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

Abstract Knowledge distillation (KD) aims to train a lightweight classifier suitable to provide
accurate inference with constrained resources in multi-label learning. Instead of directly …

Online A/B testing evaluates the impact of a new technology by running it in a real
production environment and testing its performance on a subset of the users of the platform …

We study fairness within the stochastic,\emph {multi-armed bandit}(MAB) decision making
framework. We adapt the fairness framework of" treating similar individuals similarly" to this …

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

Deep regression models typically learn in an end-to-end fashion without explicitly
emphasizing a regression-aware representation. Consequently, the learned representations …

Label ranking aims to learn a mapping from instances to rankings over a finite number of
predefined labels. Random forest is a powerful and one of the most successful general …

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