The ubiquity of recommender systems has increased the need for higher-bandwidth, natural
and efficient communication with users. This need is increasingly filled by recommenders …

Current work in planning with preferences assumes that user preferences are completely
specified, and aims to search for a single solution plan to satisfy these. In many real world …

We address how to robustly interpret natural language refinements (or critiques) in
recommender systems. In particular, in human-human recommendation settings people …

The specification of aMarkov decision process (MDP) can be difficult. Reward function
specification is especially problematic; in practice, it is often cognitively complex and time …

The past decade has witnessed a rapid growth of research on fairness in machine learning.
In contrast, fairness has been formally studied for almost a century in microeconomics in the …

Interactive recommender systems have emerged as a promising paradigm to overcome the
limitations of the primitive user feedback used by traditional recommender systems (eg …

In this thesis, we present a decision-theoretic framework for building decision support
systems that incrementally elicit preferences of individual users over multiattribute outcomes …

Current work in planning with preferences assume that the user's preference models are
completely specified and aim to search for a single solution plan. In many real-world …

We develop a Bayesian approach to concept learning for crowdsourcing applications. A
probabilistic belief over possible concept definitions is maintained and updated according to …

The goal of this chapter is to provide a general introduction to decision making under
uncertainty. The mathematical foundations of the most popular models used in artificial …