Balancing multiple objectives in contextual multi-armed bandits
Join this event on Zoom or attend a live broadcast at LSRC D106 from NC State. Snacks will be available.
Contextual multi-armed bandits are a popular framework for learning to make good decisions under uncertainty, and are applicable to areas ranging from ad placement to optimizing flu shot reminders. The majority of work in this space assumes the goal is to learn a decision policy to map from contexts to decisions in a way that maximizes the cumulative sum of outcomes of interest, such as total clicks or flu shot appointments. However, in many real world settings there are multiple objectives of interest: for instance, a stakeholder may have a limited budget, care about fairness to subpopulations, or may wish to balance the experience for those participating in a study with the generalizable knowledge that might be learned and of use for other situations. In this talk I'll discuss some of our work on learning to make decisions under uncertainty given multiple objectives of interest, and highlight motivating settings in education, healthcare and public policy.
Emma Brunskill is an associate professor in the Computer Science Department at Stanford University where she and Brunskill's lab aim to create AI systems that learn from few samples to robustly make good decisions. Their work spans algorithmic and theoretical advances to experiments, inspired and motivated by the positive impact AI might have in education and healthcare. Brunskill's lab is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford. Brunskill has received an NSF CAREER award, Office of Naval Research Young Investigator Award, a Microsoft Faculty Fellow award, and more. Visit https://cs.stanford.edu/people/ebrun/ to learn more.